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\begin{document}
\SACJtitle{Digital Forensic Science: A Manifesto}
\SACJauthor{Martin S Olivier}{}
\SACJaddress{Department of Computer Science, University of Pretoria}

\SACJabstract{
Forensic examination of evidence holds the promise of making
claims about the truth of certain propositions with the inherent
accuracy and reliability that characterises scientific
endeavours.  The propositions may relate to the artefacts
examined or related artefacts.  The nature of propositions about
which claims can be made depend on the extent to which given
propositions fall within the ambit of scientific knowledge and on
the extent to which the examined evidence is suitable for the
application of established science.  A continuing series of
incidents illustrate that in many forensic disciplines that
promise is not met --- often because some branch of forensic
science happen to not being scientific at all.  In fact, serious
assessments of forensic science have shown that many (if not
most) branches of forensic science are not scientifically valid.

Digital forensic science is one of the newest members of the
family of forensic sciences.  A number of reasons for concern
exist that it is following in the footsteps of its more
established footsteps and repeating many of the mistakes of those
other branches of forensic science.

This viewpoint is written in the form of a manifesto that is
situated in the current discourse about digital forensic science
and practice.  If challenges the current developments in digital
forensic science by positing a number of demands that digital
forensic science have to meet to be deemed scientific.  The
demands are posited as necessary, but not sufficient to ensure
that digital forensic science uses science to contribute to
justice.  Appropriate responses to the manifesto is a change in
digital forensic developments or an informed debate about the
issues raised in the manifesto.
}

\SACJmaketitle



\section{Introduction}
Most members of the public probably had a rather vague notion of
forensic science until various TV shows --- starting with CSI ---
carried an image of a forensic utopia into our living rooms on a
weekly basis.  In general we were impressed --- to the extent
that jurisdictions where juries are used had to deal with the
so-called new \emph{CSI-effect}: Juries wanted the detailed and
authoritative evidence they got used to in their favourite shows
in order to make what should have been simple decisions during
their deliberations.  Unfortunately, the reality did not match
these expectations.

While many reports suggested that much of the forensic science
on these shows was in principle realistic (apart from the speed
at which tests results became available) the computer scientists
(and some technically inclined computer users) amongst us were
usually not impressed when digital evidence needed to be
recovered.  The ability to type a command or two to trace the
exact physical location from which some message was received was
often beyond what we could accept as science fiction.

However, the disillusion was not limited to the situations where
jury members (or victims of crimes) learned that forensic science
could often not provide the answers.  A more significant problem
emerged.  In reality, various groups of people knew about these
problems for many years, but a wider audience became aware of
them as the popular media carried reports about the problem more
frequently.  The problem was that forensic science was not always
as reliable as touted.  Actually, it was worse: Much of what was
used as forensic science had no scientific basis.  These
knowledge soon enough made news headlines.  Stories of innocent
people who were wrongfully convicted based on flawed forensic
conclusions and spent much of their lives in prison (or were
executed) before being exonerated are indeed stories of human
tragedy, and deserve to be told.  Such stories also sell
newspapers.  Unfortunately, such stories cast a shadow of doubt
over forensic science in general.  The greatest tragedy of all
occurs when forensic practice in general deserves such a blanket
of distrust.

Comparative bullet-lead analysis and microscopic hair analysis
are just two examples of `forensic sciences' that were
discredited.  Currently bite-mark analysis is clinging to straws
to regain some of its former reputation.  Are finger prints
unique?  Do we really interpret blood spatter correctly?  The
simple answer, using science as a yardstick, is \emph{no}.  In a
September 2016 report (cited below) the US President's Council of
Advisors on Science and Technology reconfirmed what is obvious to
so many: They found that of the seven forensic disciplines they
assessed, almost none could be deemed to be founded on science.
(Their study only considered pattern comparison methods.)

And yet, we use those methods to send people to jail and (in some
jurisdictions) to justify capital punishment.

Much research has been done in the field of digital forensics
over the past decade.  However, where so few of the forensic
disciplines --- despite their practitioners' best efforts --- are
not scientific, self-reflection is indicated for all disciplines.

The document below describes the context and then highlights some
course adjustments that should be made if digital forensic
science want to be a `real' science.  As we move more of our
daily lives into the digital realm, and realise that we have not
yet (as of September 2016) worked out how to do proper forensic
science in the physical world (with some notable exceptions), the
need to think about digital forensic science has become
imperative.




\section{The challenge of a digital forensic science}

Forensics is the application of science to determine facts that
contribute to reaching a just deciding in a legal case.  While
the focus is on the law, these insights are also required in some
other situations, such as when the root cause of, say, an
aviation accident needs to be determined with sufficient
certainty to prevent similar accidents in future whenever
possible.  As a society we often rely on science to make informed
decisions about important matters.  In the safety and efficacy of
medicine, the prediction of severe weather conditions, the safety
of new technologies and the determination of the root cause of
disastrous accidents scientific answers are preferred over other
forms of knowledge; in fact legislation often requires scientific
proof of, for example, the safety and efficacy of a new
medication before the medicine can be registered and offered for
sale.

Forensic science, in
principle, enables one to make similar informed decisions in
courtrooms and elsewhere where the law is to be applied.
However, if forensic science is not scientific but a pretence of
science, trust in the endeavour is misplaced.  Note that even
where the word \emph{forensics} (rather that the phrase
\emph{forensic science}) is used, the notion of science is
implied; almost every academic paper on, for example, digital
\emph{forensics} contain some definition that invokes science as
an inherent foundation of such forensics.

Mistakes (or even deception) occur in all forms of testimony in
legal matters.  However, mistakes in the context of
forensics introduce a systematic bias with far-reaching effects
on the justness of the justice system.  It is not hard to find
extensive lists of examples where forensic evidence was wrong and
possibly lead to an incorrect determination that an accused was
guilty or innocent.  The Innocence
Project\footnote{\texttt{http://www.innocenceproject.org/}} is a
good starting point to find such examples; it should be noted
that they make extensive use forensic science --- in particular
of DNA evidence --- to exonerate the
wrongfully convicted.  Arguably the most thorough critique of
forensic science (including recommendations about reforming the
discipline) is the US National Academies of Science report
\cite{nas} on the state of forensic science.  In its assessment of
various forensic disciplines it repeatedly finds that the
specific discipline is not grounded in science.  A more recent
report by the US President's Council of Advisors on Science and
Technology \cite{obama} finds that almost none of the (selected)
forensics disciplines it examined meets the requirements of
scientific foundational validity.  They highlight, in particular,
that ``an expert's expression of \emph{confidence} based on
personal professional experience or expressions of
\emph{consensus} among practitioners about the accuracy of their
field is no substitute for error rates estimated from relevant
studies'' \cite[p.6]{obama}.

The discourse in digital forensics has only seen limited
self-reflection about the use of science (or scientific methods)
in its activities \cite{dft}.  While some notable exceptions
exist, the few published claims that digital forensics is indeed
scientific are often based on a limited understanding of science.

Interactions in the world in which we live increasingly occur in
the digital realm; hence one would expect that criminal
activities (and civil disputes) will increasingly rely on
evidence obtained from the digital domain.  The purpose of this
manifesto is to --- in a rather informal manner --- reflect on
the inherent qualities that a discipline needs to meet to be
viewed as a forensic \emph{science}.

For the sake of brevity we simply posit that large parts of work
done under the digital forensics label ought not to be considered
forensic science. Often a debate about such a statement reveals
that different parties in the debate view the notion of science
differently.  However, while some differences in opinion about
the nature of science will always exist, an activity cannot
simply be `designated' as scientific based on some notion of
science.  Both reports referred to above emphatically reject
various disciplines claims to be scientific --- despite the
strong belief in some of those communities that their work is
indeed scientific.

Below a number points pertinent to a digital forensic science are
raised as a basis for reflection.  They are not intended to form
a comprehensive argument about the nature of digital forensic
science, but are a reaction to some common themes in current
research in the discipline; some points provide basic background
information; others are introduced to either support or oppose
some prevalent lines of thought in the research literature.  The
manifesto provided in the final section of this paper should
similarly be seen as a document situated in the current state of
digital forensics and the current discourse of its
`scientificness'.  It is hoped that the manifesto will have some
impact on the future course of digital forensic science --- if
not by correcting inappropriate lines of inquiry, then by a
deeper reflection of how the discipline ought to proceed.

\section{Musings about digital forensic science: 
Truth, scientific truth and legal truth}
\subsection{Some remarks on when and where the `science' is
performed}

If forensic science is the use of science to help answer disputes
in legal and related matters a question that arises is when this
science is actually performed.  Consider, as a comparative
example, the amount of science that underlies the operation of a
modern motorcar.  However, this does not make the average driver
a scientist.  Most mechanics won't be deemed scientists.  In
fact, very few car service facilities or repair shops would
employ any scientists.  This does not imply that such places and
people do not possess extensive expertise in their specific
domain.  In fact, it will not raise many eyebrows if such a
person is called as an expert witness in some case.  However, the
person will not be able to testify as a scientist.

In the realm of forensic science the following scenario is
common: After extensive research a test is developed to detect
the presence of some substance in, say, blood.  Then
a device is developed to execute the test.  In a
particular case a phlebotomist will typically draw blood from an
individual, put it in the device and obtain a reading (or
printout) from the device.  A phlebotomist is not a scientist
(and, in particular, not a forensic scientist): In the UK there
is no formal qualification required to become a phlebotomist.
Very few states in the US require phlebotomists to hold any
particular qualification.  In a court case they can attest to the
fact that they labelled the blood samples correctly and operated
the test device according to standard operating procedures.
However, they are not qualified to offer any conclusions to the
court based on the results reported by the device.  Science
occurs during the development of the test.  Evidence on the
interpretation of the result (as well as on the accuracy of the
results) will have to be given by a scientist, who knows and
understands the operation of the underlying science.  In the case
of forensic laboratories the report of the test will (officially)
be prepared and signed by such a qualified scientist.

Note that the example in the previous paragraph does not imply
that the actual `testing' in the laboratory never requires a test
to be performed by a scientist.  The point is merely that many
people involved in a forensic science process on a daily basis are not 
(and need not be) scientists.   The process itself (and hence is
development) needs to be scientifically sound; the scientific
laws that underlie the process needs to be understood (and
justified) by the developers of such a process.

Checking authenticity of data using a hash function is an example
where `science in a box' may be used by a technician in a digital
forensic science laboratory. However, if the similarity is
disputed --- say due to new results about hash collisions --- the
active involvement of the scientist may be required.  A hash
function, after all, maps an infinite number of inputs to a
limited number of outputs --- and hence there will be hashes
corresponding to an infinite number of inputs.  Hence, using a
hash to claim uniqueness is far from obvious and needs an
underlying scientific basis before conclusions may be drawn that
a given match is unique.

\subsection{On the truths}

Science is a quest for truth.  The law, when considering
disputes, often need to determine facts.  Facts are claims that
are true.  It was inevitable that at some point the paths of
science and law would meet.

As the first step in a project to reassess scientific truth for
application in law it is necessary to recognise that 
two different notions of truth are involved: Scientific
truth is truth that helps to explain our world.  As we learn more
about the world, the truth often needs to be adjusted.  But these
adjustments are not arbitrary.  As an example, science explains
how aerodynamic forces impact on a body that moves through air.
Such knowledge can be used to design wings that cause sufficient
lift to keep aeroplanes in the air.  If it is at some point
determined that the scientific theories were not perfect when
that plane was built, that plane will not suddenly stop flying.
The old truth was `good enough'.  The new truth is (hopefully)
better.  (Formally: it has more explanatory power.)

Legal truth, on the other hand, is whatever the court decides.
Such a truth typically remains a legal truth unless it is changed
through some judicial process (such as an appeal to a higher
court or a change in legislation).  Legal truth is often deemed
as absolute (unless changed in such an explicit manner).  Case
law (that may date back to Roman times) in common law jurisdictions 
are deemed law until it is changed by a party authorised to do
so.

When a scientific truth changes, the use of an older scientific
truth in legal proceedings is often still `good enough'.
Problems arise where the older theory was not `good enough', but
where it was wrong.  The question to ask when science is used in
legal proceedings is not whether that science is perfect, but
whether it is sufficiently reliable. Newtonian physics, for
example, is adequate to consider the trajectory of a bullet even
though such physics is often deemed to have been replaced by
relativity theory.  In a dispute about the height of some
building or the boundaries of some property Euclidean geometry or
traditional trigonometry may be used, even when it (in principle)
proceeds from the assumption that the earth is flat.  On the
other hand, to use an old example, the discovery of the
(non-existent) planet Vulcan was a mistake (and the theory that
`predicted' its existence was corrected by relativity theory).

While science does not claim to be infallible; it is not hard to
find examples of scientific theories that were incorrect.
However, many of the failed forensic disciplines were not based
in science.  When a `non-scientific' forensic science discipline
fails it does not represent a failure of science.  It does
represent a failure of the legal system which never explored the
grounds on which such a discipline made claims that purported to
be based on forensic science.

To testify, is to express propositions that one believes to be
true.  The belief that a certain proposition is true may, in the
case of an eye-witness, be based on the fact that the witness
observed what is being testified to.  However, courts limit the
nature of the grounds on which beliefs may be based to accept the
belief as testimony.  Most courts will not accept a
belief based on divine revelation as evidence.  Similarly, what
one has heard from someone else is normally not admissible as
evidence and typically rejected as \emph{hearsay}.  In some
situations knowledge of those who have experience of actions,
context or other related matters may share their expertise with
the court as expert testimony, which may assist the court to
better understand the matter at hand.

Forensic science, in contrast, bases its belief in the truth of a
given proposition on science.  

To illustrate, the ballistic
trajectory of a projectile will follow after launch is known in
physics.  It can be calculated based on characteristics of the
projectile, the speed and direction at which it is launched, the
impact of gravity and a number of other variables.  The accuracy
with which the trajectory is calculated does not depend on the
experience of the person who performs the calculation.  It uses
theories formulated by people who may have died centuries ago and
could therefore be seen as hearsay evidence.\footnote{The first
use of scientific evidence in English Law occurred in
\emph{Folkes v. Chadd and Others (1782)} (often referred to as
the Wells Harbour case). In summary it determined that ``In an
action of trespass for cutting a bank, where the question is,
whether the bank, which had been erected for the purpose of
preventing the overflowing of the sea, had caused the choking up
of a harbour, \emph{the opinions of scientific men \emph{[sic]},
as to the effect of such an embankment upon the harbour, are
admissible evidence\ldots}''  (emphasis added)
\cite[p.157]{harbour}.} The calculation of such ballistic tables
(also known as range tables) was a routine component of artillery
used in battle.  In fact, one of the prime reasons the
development of computers became important at the time of the
Second World War was to automate such calculations.

While such a calculation is relatively simple to perform for
application in the artillery context, it may be much harder to
perform in the forensic context, where reconstruction may be the
aim.  In the forensic context muzzle elevation angle, muzzle
velocity, barometric pressure, wind speed and even the original
size and shape of the projectile may be known to a reasonable
degree of accuracy.  However, to reconstruct the trajectory (or,
at least, determine the area from which it was fired), the same
theories --- momentum, gravity, drag, drift and other
factors --- are used.  However, compared to those who fire the
projectile, the forensic examiner cannot readily determine the
values of all these variables at the time of firing, which makes
the computation significantly more complex and introduces errors,
which the forensic examiner will (hopefully) be able to quantify/

It is worth pointing out that software behaves in a very similar
manner.  It is typically easy to predict what a program will do;
however, reconstructing what a program did is much harder.  A
number of pertinent questions should be asked:
\begin{enumerate}
\item Are there scientific theories (akin to those use in ballistic
	trajectory calculation) that are useful to
	understand (or predict what will happen in)
	the digital realm?
\item To what extent (if at all) are such theories used in the
	reconstruction of events in a computing context in the
	current discourse on digital forensics?
\end{enumerate}
The latter question may be reformulated as follows: What are the
scientific theories that a digital forensic scientist can use to
justify that his or her testimony is true?  Do these theories
meet the requirements of foundational scientific validity?



\subsection{On the origins of forensic science}
An alternative route to explore the nature of forensic science is
an exploration of the roots of forensic science.  This section
explores two aspects of these roots: it explores the semantics of
the phrase and the original recognised use of science in a court
case.


Prediction observes a phenomenon (the `cause') and predicts an
outcome (the `effect').  Therefore, if $A$ (predictably) causes
$B$, and $A$ is the only cause of $B$, then if $A$ and $B$
happened, one can infer that $A$ caused $B$.  Stated differently,
$A$ now \emph{explains} $B$.  This is exactly how forensic
science uses laws to explain phenomena; forensic science is often
defined as a scientific analysis performed to determine the root
cause of one or more events.

Locard, by many seen as the father of forensic science formulated
what has become known as \emph{Locard's exchange principle}; in
his 1934 book \emph{La Police et les M\'ethodes Scientifiques}
he formulates it as ``Any action of a human \ldots{} cannot
unfold without leaving some mark''
\cite[p.7]{locard}\footnote{This is a direct translation as he
formulates it in the cited book: ``Toute action de l'homme
\ldots{} ne peut pas se dérouler sans laisser quelque marque.''}
It has been formulated in a number of ways --- often in the short
form: every contact leaves a trace.  While this principle is not
a scientific law, it works remarkably well, and in many ways seem
even more valuable in the digital realm.  If we know from science
that contact between $X$ and $Y$ leaves some trace $T$, observing
$X$, $Y$ and $T$ may enable us to explain $T$ (assuming the usual
caveats about determining causes form what are deemed to be
effects).

For such an explanation to be accepted as testimony, law 
is required to make two concessions: (a) It needs to recognise
some notion of scientific truth, that may be conveyed from one
scholar to another in a `hearsay' fashion and that science has
checks and balances in place to ensure 'truth'; these checks and
balances override the need to hear (and cross examine) the
original scholar to determine (`legal') truth; and (b) the
scientist is allowed to \emph{conclude} that the presence of $A$
explains the occurrence of $B$. This latter concession is
important because the law prefers to hear the `facts' and then
reach its own conclusions.  Now some  conclusion, based on
science rather than the law, effectively becomes a fact in the
legal process.

\subsection{On the digital}
Science, truth and reality in a sense form a triad:  Science
helps us to discover the truths about the reality in which we
live.  Conversely, if science make correct claims (in particular,
correct predictions) about the reality in which we live, science
has uncovered truth.  (Postmodernists will arguably disagree
here, but it is not clear that postmodern forensic science is
possible\ldots)

Now enter the digital realm.  This is an environment that seems
to be human-made.  Many of its prominent concepts, such as
\emph{cyberspace} derived from science fiction.  Reality in this
context may be virtual --- that is, reality may be `unreal'.
Yet, despite these idiosyncrasies, we have moved into this world
lock, stock and barrel.  Whether one views this `cyberspace' as an
alternative world, or just use the Internet for shopping, banking
and talking, does not matter.  The digital is integrated in our
lives (or vice versa).  If things in life go wrong we often need
 to prove claims we make.  And, in this integrated world, many
relevant events may have happened on the digital side.

Hence, it is no surprise that a branch of forensics --- digital
forensics --- developed to find truths about what happened in the
digital sphere.  But, unlike the physical world, there seem to be
very few rules that constrain the digital world.  In the physical
world, the rules of physics enable us to predict what will happen
(or explain what happened).  Is there a basis on which we can
make such claims about the digital space?

This is the purpose of this text: to explore \emph{which}
questions about the digital space can be answered in a scientific
manner, so that we can demonstrate a scientific truth for our
claims --- in particular claims that may be useful as evidence in
a legal context.

\subsection{Digital forensic science}
From the preceding it is rational to question whether a digital
forensic science can ever exist.  Most (if not all other)
forensic sciences deal with natural phenomena, natural substances
or human nature (which is also natural in some senses at least).
Even human-made tools are made of natural materials that will,
when it interacts with any other natural material, behave in a
predictable manner.  Note that the word ``natural'' is used
loosely here: plastics and other synthetic materials exhibit
``natural'' characteristics --- that is, in contact with other
materials, they will (to a lesser or greater degree) react in a
predictable manner --- this artificial material possesses (an
artificial) ``nature''.

In contrast, computing and the various artefacts produced by it
are as close to alchemy that humanity has ever come.  It is
trivial to program a computer to, for any inputs $x_i$, generate
any desired outputs $y_j$; it is almost equally simple to modify
`trusted' software to produce arbitrary outputs for given inputs
--- unless security mechanisms are in place that will ensure that
the software cannot be modified. Stated differently, it seems one
needs to build systems that are so secure (and correct) that they
perform as reliably and as consistently as a law of nature does.
However, I think very few experts would be willing to stake their
reputations on such an assumption that a piece of software in
infallible (or even, say, 99.999\% reliable).

The shift from digital forensic science to computing in the
previous paragraph may not seem logical.  However, digital
evidence is --- as Fred Cohen \cite{cohen-examine} so aptly
states --- a bag of bits\footnote{Perhaps the phrase ``bag of
of bit sequences'' would have been more apt.}
out of which the examiner has to extract some `evidence'.
Evidence (or, at least, meaning) may be inferred from one of only
two processes:  (1) If the bits through some justifiable process
can be arranged to form some meaningful artefact,\footnote{The
term \emph{artefact} is meant to refer to something digital
produced for later use; it forms the traces available to the
digital forensic examiner.  In a number of forensic science
branches the term \emph{artefact} refers to something
artificially introduced into a photograph or recording that was
not part of what was originally recorded; in those branches
artefacts are ignored as artificial additions to recorded
observations.} then meaning
has obviously been found.  Alternatively, (2) if the bag of bits
is the result of some computational process it may sometimes be
possible to make claims about the inputs to that process and/or
the process itself.  

\newtheorem{conjecture}{Conjecture}

\begin{conjecture}
\label{con:claims}
Digital forensic science claims can only assume one or both of
two forms, namely
\begin{enumerate}
\item That the digital data examined is an example of a
specific class of artefact; and/or
\item That the digital data examined proves or disproves a claim
that the data was the result of specific data transformed by a
specific computational process.
\end{enumerate}

More formally these two claims may be stated as follows:
\begin{enumerate}
 \item For some `recognised category' $C$ (to be elaborated on later)
and some sequence of bits $s$, the digital forensic scientist
can \emph{conclude} that $s \in C$; and/or
 \item That, given some computational process $P$, some inputs
$x$ and some output $s$, the digital forensic scientist may
\emph{conclude} that, depending on the specific values, $P(x)$
did or could have produced $s$.
\end{enumerate}

\end{conjecture}

Both of these claims, for the sake of simplicity, have been stated
in a somewhat more limited form than intended.  This will be
addressed below.  In fact, it will be shown that in this limited
form the conjecture has much wider application than what it may
seem initially.

To `prove' this conjecture in one direction (namely that
scientific forensic claims can indeed take one --- or both --- of these
two forms) examples will suffice.  However, conjectures are
conjectures because they cannot (yet) be proven; to convince the
reader that the examples to be provided are indeed correct, we
need conjecture \ref{con:design} to be introduced below.
`Proving' the conjecture in the other direction is harder and may
indeed be shown to be false.

Note that conjecture \ref{con:claims} refers to
digital forensic science specifically; it excludes branches of
forensic science that may deal with digital artefacts, but where
the claims made are not in the digital realm.  Thus the intention
here is \emph{not} to deal with, for example, voice recognition
or authorship attribution of a recording or a document,
respectively, that happen to be in a digital format.  Those
branches include `natural' properties (such as the properties of
human speech or the vocabulary and style used to compose a
document), and hence do not face the same challenges as `pure'
digital forensic science.

A defence of the conjecture that these are the only two valid
forms of scientific forensic claims will be attempted later.
However, to proceed in the former direction (that there are
indeed two forms) another conjecture is required, which will be
colloquially formulated as follows.

\begin{conjecture}
\label{con:design}
In digital forensic science the notion of `intelligent design'
will often be sufficient to correctly classify an artefact.  The
degree of certainty with which this can be done depends on the
nature of the class.
\end{conjecture}

To illustrate this conjecture, suppose that an investigator
obtains a set of bytes for which some reasonable grounds exist to
infer that a subset of the bytes are intended to be interpreted
in a given fashion.  To make this concrete, suppose one obtains a
sequence of bytes from a system that are purportedly a JPG file.
The claim (or hypothesis) that it is a JPG file may come from the
file extension (if the file name is available), the initial bytes
of the sequence and/or a variety of other clues.  Conjecture
\ref{con:design} claims that we are able to determine whether the
sequence of bytes  indeed a JPG file or not and make that
claim with a specified degree of certainty.

Of course, if the sequence of bytes conforms to all syntactic and
semantic requirements for a JPG file, it opens in a variety of
JPG viewers and (possibly) yields an identifiable picture, the
sequence is a JPG structure, without any doubt.  The only source
of uncertainty is whether it existed on the medium from which it
was retrieved as a JPG file.  This is where the level of
certainty needs to be determined.  It is extremely unlikely that
a random sequence of bytes from a medium will form a JPG image.
On the other hand, in the unlikely case that one tries all
permutations of subsets of bytes on a medium, the likelihood that
one of the permutations will conform to the JPG specifications
increases dramatically.

Another example may be useful: if one recovers an 8-bit value
from a medium it obviously can be a member of the class of 8-bit
unsigned binary numbers.  The question whether it existed (that
is, was used) as an 8-bit binary number on the system in question
can only be answered after much more context has been studied.

Clearly, the likelihood of error does depend on the complexity
of the artefact being examined: A JPG file has a header which not
only has a standard format, but also has fields that impact the
interpretation of the remainder of the file.  As noted, when
opening the file in an image viewer one would normally expect to
see an intelligible image.  If this is true of a file confidence
grows that we indeed have a JPG file.  A series of additional
checks may be desired, such as the EXIF metadata to increase
confidence --- if required.  In contrast, other formats may have
much less inherent structure (effectively, much less
redundancy/meaning) and it may be much harder (or even
impossible) to confirm whether they exhibit ``intelligent design
traits.''


It is now time to return to conjecture \ref{con:claims} to fulfil
the promise that examples of the two conjectured claims would be
provided.  For the first form the example alluded to will
suffice: If the investigator obtains a file that claims to be a
JPG image (say, through its extension), determines that it
conforms to the rules and specifications of a valid JPG image
and, when opened with an image viewer displays what is clearly a
picture, the conclusion that the file is indeed a JPG file is
obvious.  The contents of that file can then be reproduced in a
form that will enable the court (or some suitably qualified
expert) to make its findings.  In some instances the digital
forensic scientist will be qualified to do this, given the second
form of conjecture \ref{con:claims}.

As an example of the second form of conjecture \ref{con:claims}
consider the case where the computational process $P$ is the
calculation of a known reliable hash function and the input $x$ is a
sequence of bytes.  Then the digital forensic scientist may
conclude that $s$ is (or is not) the hash of $x$.  To say that
$P(x)=s$ is straightforward; however, the intention is also to
conclude that $P^{-1}:s \mapsto x$, which needs to be qualified
by the confidence (or error rate) of such a claim, because this
is inherently a probabilistic claim.  However, note that this
example does not suggest that $P$ should be a standard,
well-studied computation: $P$ may, for example, also be a piece
of malware never encountered before.

Given the fact that these two forms of claims are used on a daily
basis in digital forensics no further elaboration is required to
substantiate their utility.  What needs attention is their
sufficiency and (eventually) a stronger justification that there
is a scientific basis for (some) such claims.

\section{The manifesto}

The manifesto that follows represents the insights that may be
gained from the discussion in the preceding sections.  Not every
point contained in the manifesto can be deduced in full from the
preceding discussion, though.


\newcounter{mi}


\subsection*{Forensic science}
\begin{enumerate}

\item The term \emph{forensics} refers to forensic
\emph{science}.  Any notion of a non-scientific forensics
contradicts a generally held understanding in the academic
literature and by the general public of forensics; such a notion
would inherently cause confusion.

\item The utility of science in forensic science is the ability
of science to explain phenomena.  The explanatory ability of
science is inherently related to its ability to (correctly)
predict.

\item The reliability (or accuracy) of a forensic science (or
forensic discipline) is limited by the accuracy with which the
underlying science can predict.

\item The term \emph{science} is contested and the problem of
demarcating science remains critical.  Philosophy of science
provides many useful insights.  In addition, standard scientific
practices, such as peer review, provide a practical basis for
demarcation.  Both the appropriate nature and appropriate
practice are necessary elements to denote an activity as
scientific.

\item Forensic science ultimately has to explain why an event is
seen as the root cause of other events.  Forensic science
therefore needs to be a science (or an application of a science
or based on a science) that (a) can justifiably claim to be a
science, and (b) has explanatory --- and hence --- predictive
abilities.

\setcounter{mi}{\theenumi}

\end{enumerate}

\subsection*{The digital realm}

\begin{enumerate}

\setcounter{enumi}{\themi}

\item Computing is used in many branches of forensic science,
such as matching exemplar fingerprints with those stored in an
extensive database or visualising physical phenomena in various
ways.  The fact that computers (and, hence, digital
representations of phenomena) are used does not imply that
digital forensics is being used.  In these cases computing is
used to support some forensic test.  If a category descriptor is
required for such computing the phrase \emph{forensic computing}
accurately reflects the activity.

\item The phrase \emph{digital forensics} is commonly used to
describe an examination of digital artefacts that \emph{exist as
digital artefacts} (rather than physical artefacts that have been
converted to digital).  To emphasise the point, fingerprints that
have been transferred to paper are not examined as paper
forensics; similarly fingerprints that have been converted to a
digital representation do not form part of digital forensics.
One possible characterisation of digital forensics is that it
examines events (or traces of events) that happened in
the digital realm; the purpose of digital forensics then is to
determine the root cause of, or to reconstruct events that
happened in cyberspace.

\setcounter{mi}{\theenumi}

\end{enumerate}

\subsection*{Examinations and investigations}

\begin{enumerate}

\setcounter{enumi}{\themi}

\item  Forensic examinations punctuate investigations.
An investigation (such as a police investigation or a
criminal investigation) typically includes many activities that
are not scientific (and that cannot be scientific).
Investigators often follow leads that are wrong or based on
unreliable evidence.  Decisions about which leads to follow and
when to abandon a specific line of investigation are often not
based on objective criteria.  The investigators' experience,
intuition, the legal requirements of obtaining search warrants
and other permissions, the behaviour of those implicated by a
case and many other factors determine the course of the
investigation.  It is expected that the investigation will
uncover relevant facts.  The role of forensic science is to test
hypotheses (or theories) that arise for (scientific) factuality.
Investigators work with leads that range from unlikely to proven
facts.  The bar for considering something a lead is low, but
leads may differ in strength; a strong lead may need much
stronger grounds to be considered a strong lead.  Do note that a
proven fact may be a weak lead --- if it, for example, turns out
to be irrelevant.

\item The phrase \emph{forensic investigation} is usually a
misnomer.  This phrase is often used when disasters (such as
airplane accidents) are investigated.  The phrase may allude to
the fact that forensic science often fulfils a major function in
such investigations.  However, such investigations include many
non-scientific aspects (such as interviews with eyewitnesses and
survivors).  Forensic examinations are typically conducted by
laboratories best equipped for the specific test to be performed;
in the case of major disasters, many forensic facilities in many
countries may be involved, where each focusses on one component,
one category of residue or some other specific facet of the
investigation.  The investigators work on the investigation; the
forensic laboratories conduct their specific analysis limited to
the question raised by the investigation team.  In order to
minimise confusion it is best to explicitly distinguish between
forensic examinations (or forensic analyses) within the context
of a (non-forensic) investigation.

\item With forensics being inherently scientific, care should be
taken to not refer to non-scientific procedures as forensic
activities. Many forensic processes may be useful during an
investigation, but the converse is not always true.  Forensic results will, in
general, be admissible in court as evidence; leads and
investigation results will not be admissible as facts unless
sufficiently corroborated.  Hence the investigator should clearly
understand the difference between the two.

\item The word \emph{evidence} should similarly be used with
care.  On the one hand, evidence may be whatever is collected in
relation to a crime --- whether it will have probative value or
not.  On the other hand \emph{evidence} may be something that
proves a claim; this is the type of evidence handed up in a court
of law.  In a forensic context, evidence ought to have the
second meaning --- (forensic) evidence will, in particular, be
evidence about which forensic truth claims are made.

\setcounter{mi}{\theenumi}

\end{enumerate}

\subsection*{Independence}

\begin{enumerate}

\setcounter{enumi}{\themi}

\item The use of forensic science is not limited to the criminal
justice system; many forensic disciplines (with digital forensics
a prime example) ought to be of use in civil matters, internal
hearings and other contexts where such evidence may contribute to
justice.  This ought to be reflected in the language used to
report research results; words such as crimes, guilt and
innocence should be used judiciously in research on the topic
since they ultimately affect the research agenda and application
of forensic research.  (This neither means that these words
should be avoided at all costs, nor that work that is of
particular use in a given context --- criminal, civil, or other
--- should be discouraged; however, many forensic procedures will
be applicable to more than one context, and its exposition and
development should not be limited by unnecessary suggestions of
context through examples, terminology or other potential biases.)

\item 
Forensic science is ultimately in the service of justice, rather
than specific users of forensic science.  Too often forensic
science research focuses on its use in law enforcement (as the
most prominent example).  Digital forensic science will often be
more useful in corporate contexts than most other forensic
disciplines.  Care should therefore be taken that the digital
forensic research agenda is balanced and serves the interests
both of those with and without access to resources.  One way of
gaining neutrality is for any work that produces a mechanism to
prove some proposition $p$ to also reflect on how to prove
$\overline{p}$ or to prove some proposition $q$ that could serve
as a rebuttal of the claim that $p$ happened.

\item
Neither law enforcement, nor the corporate sector is, in general,
equipped to do scientific research (with some significant
exceptions).  Hence the `natural home' for forensic science
research are the traditional research institutes, such as
universities.  Funding from industry or law enforcement should be
recognised as possible sources of bias (if not in the research
itself, then in the research agenda).  Hence any such sponsorship
should be explicitly declared as potential conflicts of
interest.\footnote{This is standard practice in most medical
research and already strictly enforced by the American
Academy of Forensic Sciences.}  In the ideal world digital
forensic research will be funded by government or other bodies
who have little reason to prescribe

\setcounter{mi}{\theenumi}

\end{enumerate}


\section{Conclusion}
Manifestos are often written by authors who deem it necessary to
assume and express a position at a time when they perceive a
danger that, if such an option is not expressed an opportunity
will be lost to impact the direction of some discourse.
The use of the term \emph{manifesto} indicates strong
convictions of the author that (a) an adjustment of the course is
necessary and (b) the issues that are raised in the manifesto are
those that ought to be high on the agenda for reflection. In this
sense a manifesto is contextual; it speaks to the current
discourse, rather than provide a conclusive, comprehensive
perspective on the topic at hand.

In this manner, this manifesto does not attempt to define digital
forensic science.  Its intention is to highlight necessary (but
not sufficient) aspects of a digital forensic science.

A manifesto is, by nature, a conviction set forth by its
author(s).  A conviction does not claim to be absolutely correct,
but issues a challenge to others participating in the discourse
to engage in further discussion on the points raised in the
manifesto.  As a conviction, it is a call for change in the
current course of events.  The weaknesses of forensic science
have made newspaper headlines over many years, but the news media
are often easily dismissed as being more interested in
sensationalism, rather than facts.  However, when organisations,
such as the US National Academies of Science and the United
States President's Council of Advisors on Science and Technology
raises serious concerns about the absence or lack of
(foundational) science in most of the forensic science
disciplines that they considered, it is a loud and clear signal
that introspection is required.  The two reports issued by these
organisations that were cited above say very little about digital
forensic science, it does not absolve the digital forensic
community from introspection and a well-considered response.
Hopefully the manifesto above posits claims that will indeed lead
to reconsideration of the `old' answers to the issues raised
(where) such answers exist, and reflection on the `new' issues
raised.

\section*{Acknowledgements}
I would like to thank a number of colleagues, friends and family
members who read, commented on (and critiqued) the manifesto.
They will remain anonymous to protect them from the mob of
digital forensic practitioners who may be upset by the
publication of this manifesto.  You know who you are.  I
appreciate your assistance.

\bibliography{dfm}



\end{document}

\chapter{On the scientific bases for digital forensic science}

\section{Algorithmics}

The example regarding hashes already alluded to the first
possible basis for scientific deduction in a matter dealing with
digital forensics.  Let us use the term \emph{algorithmics} for
this category.  We know how easy or hard it is to solve many
computational problems.  Much of that is based on the (unproven)
Church-Turing thesis.  Despite being unproven, this thesis is
falsifiable (as the term is used in Popper's description of
science), is widely accepted and relied upon.  From this (and
some other theses) algorithms have been developed to digitally
sign documents, encrypt data and perform other operations used
(and relied upon) daily in the world of e-commerce and related
contexts.  In some cases flaws are discovered in specific
instances and some algorithms have been found to have inherent
weaknesses.  However, by en large, such algorithms exist that are
as trusted as other artefacts derived from science.  Often such
algorithms rely on non-disclosure of data, such as a key.
If not disclosed the possibility of finding it through a brute
force (or, sometimes, other form of attack.)  However, for a
brute force (and sometimes other forms of attack) it is possible
to express the probability of a successful attack --- which
translates to a calculable error rate (in the absence of unknown
attacks, where such attacks may be an option).

In general algorithmics enables the scientist to determine the
ease with which a given value can be computed (or even the
impossibility to compute a certain value).  

Categories of interest include those where a value may be hard to
compute, but easy to verify once found, as well as those where
verification remains hard even if a candidate value is found.  An
encryption key may, for example, be hard to compute; however,
once a candidate is located and the candidate decrypts encrypted
messages to ones that are meaningful the candidate has been
verified.  On the other hand, once a purported prime number has
been found, verifying the claim is intractable; however,
verifying it using probabilistic algorithms (with a known error
rate) is indeed possible.  An example of this option with more
obvious digital forensic application will be provided elsewhere.

\section{Class characteristics}

A key question that often arises (and often seems easy to answer)
is the question whether a found artefact is an instance of a
given class.  The question is not often formulated in this
manner, and, this formulation is reminiscent of object-oriented
language; however, our intention is to formulate the question
using typical forensic terminology, where the terms \emph{class}
and \emph{class characteristics} are indeed common.

In digital forensics this question may manifest itself in the
context of file systems (``Is this an NTFS disc?''), files (``Is
this a JPEG image?"), system constructs (``Is this a Windows XP
registry?'') and so on.  Superficially this type of question
seems straightforward to answer (in many or even most cases).
However, the question is inherently ambiguous and needs to be
explored further --- both in terms of intent of the question and
the certainty with which it can be answered.

To illustrate this point the question may arise whether a file on
a  system is a picture that constitutes contraband.  Opening the
file with an image viewer may `reveal' an image, which, in
principle, terminates the digital forensic part of the
investigation; an expert on contraband images (or the court) is
now in a position to determine whether the image does indeed
constitute contraband.  However, if the image viewer is unable to
open the file one may rightfully ask whether this is sufficient
proof that the file does not constitute contraband.

Let us explore this question somewhat deeper.  Real-world
artefacts are often recognisable by (in Wittgenstein's terms) a
\emph{family resemblance} they share with one another.  A
modern telephone shares few similarities with one from a century
ago, but human beings view them in the same class regardless.
Digital artefacts, in contrast, are often precisely defined, and
even if only one bit out of thousands changes, it may no longer
be recognisable as such.  In other cases significant changes may
be made, with the artefact at least remaining recognisable.
Syntax is the first facet that makes a digital artefact
`recognisable'.  The format of data messages are often described
in the form of a grammar.  Bakcus-Naur Format (BNF), or some
extented variant, EBNF is one common way of describing the
format.  Other options are ASN.1 (Abstract Syntax Notation 1), a
description of a data record using a (mathematical) grammar, or,
for data constructed based on an underlying formalism, such as
XML, a schema, Data Type Definition or similar structure may be
used to define the precise grammar for the stored or transmitted
artefact.

To make matters more concrete, it is, for example, possible to
verify the syntax of an image, word processor document or email.
If it parses without errors one may conclude that this artefact
is --- at least syntactically --- a member of its identified
class of artefacts.  Since such artefacts are produced by
software, it is reasonable to expect that related artefacts will
usually adhere to such a common syntax.  Consider, for example, a
popular word processor format.  Then all word processors using
this format \emph{should} use a shared syntax (at least) to
represent documents.  Making a few random changes to such a
document with a low-level editor may damage it to the extent that
the word processors will no longer be able to open it, arguably
removing it from the class of valid documents.  However, in
another instance, modification may still leave the document
usable, but possibly with contents somewhat changed.  Hence given
some format ``$D$'' it is possible that the following `related'
classes exist:
\begin{enumerate}
 \item An `ideal' class $D_I$ of all `strings' that conform to
	 the (formal) specification of $D$.  In the ideal case
	 the specification may be a grammar $G_D$ and $D_I$ is then the
	 set of all strings that can be generated by $G_D$,
	 conforming to all semantic constraints.
 \item For any program $P$ that is able to read documents in
	 format $D$, let $D^r_P$ be the set of all documents that $P$ reads
	 and interprets correctly.  Note that the term
	 \emph{correctly} here simply means that the program
	 operates without failing.
 \item For any program $P$ that is able to write documents in
 	 format $D$, let $D^w_P$ be the set of all documents that
	 $P$ can generate.
\end{enumerate}

Note that the nature of $P$ is not fixed \emph{a priori} --- it
may be a specific version of commercial software (including
dynamic libraries shared with other software with given patches
applied), it may be software (commercial or otherwise) found on a
given computer, it may be a conceptual algorithm, and so on.


... symbolic execution
.... symbolic execution signatures ??

... differential attribution (class attribution vs individual
attribution)

... edit distance



Note that some documents are inherently complex and the mere fact
that a string of bytes conform to such complex rules instil a
level of confidence that one has indeed classified the artefact
correctly.  The odds that a random string of bytes will conform
to the rules placed on a JPEG image are infinitesimally small.
However, using a random string as the starting point for such a
calculation would normally be misleading.  Data will generally be
`chunked': on disc the bytes in a sector will generally retain
their sequence.  When sectors are grouped into clusters, the
order of data in a cluster can generally be expected to be in
their original order, but the clustering method may be an
inference.  On networks data usually arrive in packets with some
error checking code that `vouches' for the integrity of the
packet.  Of course, data is malleable and a human (amongst
others) can fabricate data that violates almost any assumption
that can be made.

However, the forensic findings discussed in this section do not
deal with authenticity of an artefact, but just with the
identification of artefacts (including identification of
artefacts that deviate in some way from some expected norm).

Keep in mind that digital artefacts are often containers for
other artefacts: a disc is a container of partitions; partitions
contain file systems; file systems contain files; often files are
wrappers that encapsulate related information (such as cover art
and other metadata in, say, music files or thumbnail images in
full-size images).  A top-down approach may verify the integrity
of each of these containers before the contents are inspected.
Confidence about the integrity of the container yields more
confidence in deductions that are made about the contents of such
containers.  As an example of this latter point, once the
integrity of a file system has been established one can use the
metadata linking the blocks on that disc into files with a higher
degree of trust than would otherwise have been the case.  Note
that the top-down order is also not mandatory: where time is of
the essence it may often make sense to accept, say, the integrity
of the file system as a starting point to facilitate quick
discovery of useful information; the integrity of the file system
may then be examined later, while the initial leads are pursued.
If the integrity of the filesystem is confirmed, the initial
assumption is validated; if concerns are later raised about the
integrity of the filesystem the `speculatively' uncovered
evidence can be discarded ---  it, at least, provided avenues
that could be (sometimes successfully) explored during time that
would otherwise definitely have been wasted.

Also, as noted earlier, the degree of scrutiny afforded to any
artefact depends on what one wants to learn from that artefact.
In many cases the mere fact that an image viewer displays an
image when presented with an artefact will be sufficient to
classify the artefact.  In other cases much closer scrutiny may
be warranted.

\section{Prevalence}
The notion of prevalence is problematic in any artificial
environment.  At one time it was possible to detect much spam because
spam exhibited certain obvious characteristics.  Certain words
(such as the phrase \emph{You have won \ldots in the \emph{X}
sweepstakes}, where X was the name of a well-known company, law
or familiar phrase, was an immediate indicator of spam.
Similarly, specific ``from'' addresses were known spam senders.
However, the ease with which such spam could be detected quickly
meant that such spam was filtered and no longer reached a huge
proportion of their intended audiences.  Prevalence of such spam
therefore declined, and more sophisticated spam (such as
personalised spam) became more common.  It is safe to assume that
any patterns in spam that can be learned by spam detectors can
also be learned by spam senders and that any detected patters can
be removed so that spam can bypass filters based on such
patterns.  It is also safe to assume that sophisticated spammers
implement ``quality control'' mechanisms in which the ability of
spam sent to penetrate the best known defences are continuously
monitored and remedial steps taken where required.  In artificial
systems it is much easier to immediately initiate new behaviour
or implement new characteristics.

In many branches of forensic science prevalence is a key element
of the science.  In DNA identification the prevalence of short
tandem repeats at specific genetic loci in a given population is
a key element of the accuracy (or error rate) that DNA
identification can claim.  Even though this prevalence may be
known to criminals there is nothing they can do to modify it.
Given the inherent distinction between natural and artificial
prevalence the question arises whether prevalence plays any role
in forensics that deal with an artificial system.  In simplistic
terms: are (prevalence) statistics relevant to digital forensics?

One response may be that a concerted well-resourced global
initiative may be able to provide real-time prevalence statistics
about threats (in particular, malware, active exploit abuse,
spam, and so on), about installed systems, about the usage of
online services, as well as almost any other factor of interest.
However, forensic science often depends on an assumption that a
random individual from that prevalence pool is the subject of a
forensic examination.  It is (in most cases) unlikely that the
individual involved in some incident will have been selected
based on a specific occurrence of short tandem repeats in his or
her genetic makeup, or on swirls and whorls in their
dermatoglyphic makeup, or be an outlier in terms of blood-spatter
traces.  However, in the case of artificial systems, the tools
used, the selected target and/or the strategy used may be due to
a specific choice made by the actors in the incident; the fact
that any of these is an outlier in statistical terms does not
translate to a numerical probability of uniqueness or any other
similar claims about accuracy or error rates.




***********************************************************


In forensics prevalence is often useful when a combination of
independent conditions or markers are present in an artefact.
This is precisely the approach followed by DNA identification.
The prevalence of short-tandem repeats (STRs) at specific loci on
human genetic material in a given population is relatively easy
to determine.  If a, say 10\%, of the population has an STR on a
specific locus and DNA material found at a scene has an STR at that
locus, the proportion of potential donors of that genetic
material is narrowed down to 10\% of the population.  If the same
found sample as an STR at another locus of interest and, say, 8\%
of the population has an STR at that location (and presence of
this STR is independent of presence of the other STR discussed
earlier), then the donor of that DNA sample is one of only $10\%
\times 8\% = 0.8\%$ of the population.  Once presence (or
absence) of STRs at about ten loci of the sample
(with the exact number dependent on standard practices a given
country) has been determined, the expected number of persons with
that genetic makeup is a tiny fraction of one person.  Hence,
finding a matching donor is statistically highly improbable.
However, the donor of that sample will, of course, match the
sample.  Hence, when a matching suspect (or other party of
interest) is found what is statistically highly improbable has
been achieved --- and it is fair to conclude that the match is
not a random event, but caused by the fact that the matching
individual is indeed the donor of the sample.  Moreover, the size
of the ``statistically expected'' group is a measure of the
possibility of a false positive.

The question that arises is whether similar prevalence data can
be used to reach similar conclusions about digital devices.
Possible ``markers'' may include the operating system used on a
device, ports that are open, software that is installed, physical
interfaces and peripherals, configuration settings and a plethora
of other characteristics. To illustrate, the prevalence of
operating systems is relatively easy to determine.  During an
investigation it is possible to determine the operating system of
a seized device relatively easily by inspection.  Network
scanners such as \texttt{nmap} have a remarkable ability to
determine the operating system of a device via a network (to a
specific (minor) version) --- unless the machine has been
properly secured against footprinting.  Media and/or data files
often identify the operating system used to create or write them.
Therefore one may be able to determine a specific marker (such as
operating systems) on the device itself, remotely via a network
and/or on artefacts created by such a system, providing a first
step to link them with a known error rate.  Moreover, operating
system prevalence is a social phenomenon and social phenomena
tend to be more stable over time than digital phenomena.

A second example of a marker (or perhaps a second set of markers)
may be the WiFi access points a device attempts to connect to.
Presumably (but not yet proven) such access points are
independent of operating system.  There are a couple of scenarios
to consider.  Some major service providers deploy access points
with the same name (or SSID) over major parts of the world.  The
market penetration of these providers are relatively easy to
determine.  Such statistics narrow down the portion of devices
that exhibit given markers.  A related but also very different
remark can be made about access point deployed at private homes
and other small contexts.  The existence of databases of such
private access points is well known.  Typically prevalence
statistics per access point are not available, but, being private
access points, they tend to narrow the population of matching
devices significantly.  However, being private, the choice of
operating systems used on them will often no longer be
independent of the access point name --- and multiplication of
prevalence statistics is no longer meaningful.

In fact the independence requirement is arguably bigger that it
may initially seem.  It is precisely the specific choices made
for network and other parameters made by operating system
designers that enable software such as \texttt{nmap} to
successfully identify operating systems; stated differently, it
is the close coupling between system version and network
behaviour choices that facilitates system identification; this
indicates dependence, rather than independence.

The WiFi example also illustrates a second concern: While WiFi
traces may also be present in various contexts (on the device
itself, in the vicinity of the define of it broadcasts access
point names in search of the access points and on embedded hardware,
such as WiFi-enabled SD cards) the traces are left in general in
a very different set of artefacts than the operating system
traces.  This indicates that it may be hard to identify a set of
markers that is sufficiently general to link associated artefacts
that exhibit dissimilar class characteristics.

It is a relatively simple task to add additional concerns to this
short list; this paper will, however, refrain from doing so.  In
summary, prevalence data \emph{may} be useful link artefacts
where such linking can be achieved with known error rates.
Surprisingly many prevalence measures are more stable than
expected, because they really depend on social phenomena.
However, the range of possible markers and independence between such
markers seem like major challenges.

Perhaps even more importantly, while relatively stable prevalence
indicators exist, the ease wit which these markers can be
manipulated on a device used in the commissioning of a crime is
a major concern.  We cannot conclude that prevalence has no role
to play in digital forensics; however, to utilise prevalence
poses significant challenges.  Perhaps prevalence will be
important in some niche forensic applications.  Until its utility
is demonstrated a pessimistic view of prevalence-based techniques
seem realistic.


























\chapter{The manifesto --- to follow RSN}

The purpose of this document is to derive a manifesto as an
artefact to discuss.  The intention is to derive it from the
various forces (forensics, truth, reliability, and so on) that
emerge from the document thus far.  At this stage I am still
working on this derivation process --- and, hence, do not include
a manifesto yet.  My intention is to fill this empty chapter with
at least some draft as soon as possible.

\appendix




\chapter{bits and pieces}


  Jeon BR, Seo M, Lee YW, Shin HB, Lee SH, Lee YK (2011). "Improving the blood collection process using the active-phlebotomist phlebotomy system". Clinical Laboratory 57 (1-2): 21–7. PMID 21391461.
draws the blood

***********************

\section{Expert witnesses}
Above two characteristics of forensic science have been
identified, namely the (a) admissibility of (specific forms of) hearsay
evidence; and (b) permission to include conclusions in testimony.
(Again this summary should be augmented by disclaimers,
qualifications, explanations, exceptions and so on to be
considered a correct claim; however, our goal here is to
provide simple handles to invoke these notions --- along with any
baggage that may be associated with them).  In what follows, the
phrases \emph{hearsay evidence} and \emph{derive conclusions}
will be used to denote these concepts.

A court may rely on the testimony of an expert witness; in many
jurisdictions the bar to be an expert witness is set relatively
low: anyone who knows more about a specific topic than the
general public may be deemed an expert.  A homeless person, for
example, may be an expert on street living.  This person may
provide the court with insights about the hardships of street
living, possible ways of obtaining food and shelter, community
life on the street, and so on.  However, this person will not be
allowed (in general) to present hearsay evidence and this person
will not be allowed to apply his or her experiences to the case
being tried and offer some conclusion based on expertise.

Let us consider another example: suppose someone wants to protect
a home from being burglarised --- in particular by ensuring that
the burglars are caught.  This person decides to rig a paintball
gun near a window used in the past by burglars, such that anyone
who enters through the window will be shot with paint with, say,
a fluorescent pink hue.  Sure enough someone breaks in and the
gun fires.  Our homeowner informs the police that they may find
someone wandering the streets literally lighting up the night in
pink.  Soon afterwards a glowing pink suspect is apprehended.
Irrespective of the homeowner's expertise, testimony will be
limited to the homeowner's actions (or experiences).  Conclusions
about whether the glowing pink identifies the burglar will be the
court's prerogative.  (And if the gun injured the intruders at
all, a new crime may suddenly be investigated, where the
purported intruder claims medical and other costs from the
plaintiff To bring it somewhat closer to forensic
sience, in ... vs ... in the USA the court was far from convinced
that fingerprint identification is rooted in science.  Hence the
expert witnesses were instructed to only testify about what they
saw, did and experienced.  However, they were barred from
testifying in any way that the sets of fingerprints (collected
from the suspects and found at the crime scene) \emph{matched}.
Testimony about a match would have required them to apply a
theory (conveyed by trainers and books to trainees, that is, it
would have required them to  violate a theory that was --- at
that moment not deemed scientific and hence subject to the
`normal' rules that apply to expert testimony.





***********************

 To illustrate, consider the following list of translations of
 ``Science and technology'' and of ``technology'' (where the
 suffix \emph{-logy} is interpreted ``study of'', as it usually
 does) in a number of languages.
 
 \begin{tabular}{lll}
 \textbf{} & \textbf{Techn\'e} &\textbf{``Technology''} \\
 \hline
 Afrikaans & Tegniek & Tegnologie \\
 \textbf{English} & \textbf{Technology,craft,art} & \textbf{Technology} \\
 French    & la science et la technologie &\\
 German    & Technik &\\
 Greek     & $E\pi i\sigma\tau$\\
 Spanish   & Tecnología  &\\
 Zulu      & & \\
\end{tabular} 
 
 
 Van Dale zegt over technologie het volgende: tech·no·lo·gie (de ~ (v.), ~ën)
 
 leer van de handelingen waardoor de mens de voortbrengselen van de natuur tot stoffen verwerkt tot bevrediging van zijn behoeften
 systematische toepassing van een wetenschap in de techniek[2]
156,202d180


********************************************



https://aquileana.wordpress.com/2014/02/01/aristotles-three-types-of-knowledge-in-the-nichomachean-ethics-techne-episteme-and-phronesis/




In the Dictionary of Philosophy,  it is defined as: “The set of principles, or rational method, involved in the production of an object or the accomplishment of an end; the knowledge of such principles or method; art. Techne resembles episteme in implying knowledge of principles, but differs in that its aim is making or doing, not disinterested understanding”. 

Characteristics: Pragmatic, variable, context-dependent. Oriented toward production. Based on practical instrumental rationality governed by a conscious goal. The original concept appears today in terms such as “technique” and “technology.” 
...


3.?Phronesis It means Practical wisdom. It is related to the following main ideas: Ethics.  Deliberation about values with reference to praxis.  Pragmatic, variable, context dependent.  Oriented toward action.  Based on practical value-rationality.

Aristotle distinguished between Sophia and Phronesis in the following manner. Sophia involves reasoning concerning universal truths, while Phronesis includes a capability of rational thinking. 

In order to practice phronesis, Aristotle felt that political abilities were required, as well as thinking abilities. Aristotle categorized there elements of character (ethos) in the following manner: 1) phronesis (how to act in particular situations), 2) areté (virtue) and 3) eunoia (goodwill).

***********************************************************


********************************************

 In The Nicomachean Ethics, Aristotle (384 /322) describes three approaches to knowledge. In Greek, the three are episteme, techné and phronesis. 
 
 Whereas episteme concerns theoretical know why and techné denotes technical know how, phronesis emphasizes practical knowledge and practical ethics.
 
 Aristotle classified knowledge in three different types Episteme (Scientific Knoledge), Techné (Skill and crafts) and Phronesis (Wisdom).
 
 1.?Episteme: It means “to know” in Greek. It is related to scientific knowledge. Attributes: Universal, invariable, context-independent.  Based on general analytical rationality. Epistemology, the study of knowledge, is derived from episteme. 
 
 Episteme was viewed by the Greeks as a partner to techné. Plato used episteme to denote ‘justified true belief”, in contrast to doxa, common belief or opinion.
 
 2.?Techné: The greek word translates to craftsmanship, craft, or art.
217d204
 ...
218a206,208
 For the ancient Greeks, when techné appears as art, it is most often viewed negatively, whereas when used as a craft it is viewed positively because a craft is the practical application of an art, rather than art as an end in itself. In “The Republic”, written by Plato, the knowledge of forms is the indispensable basis for the philosophers craft of ruling in the city.
 
 Aristotle viewed techné as an imperfect human representation of nature. Socrates and Plato also used the word, and distinguished craftsmanship (which they viewed in a positive light) from art (which they viewed in a negative light). 


*************************************************************

It seems that crime and, in particular, solving mysteries caused by
crime, fascinated humanity from the beginnings of time.  This
fascination may be observed in the continued production of
fiction where sleuths solve crimes using their keen
powers of observation, mind-boggling deductive skills, paranormal abilities
and/or requesting the intervention of the gods.  In most of these
tales evidence plays an invaluable role.  However, the use of
scientific evidence is a more recent phenomenon that evolved into
storylines where evidence --- through science --- solves crimes.
Period.  Forensic science entered the popular discourse.  And, so
it came to be that public insight and public misconceptions about
forensic science were both on a growth path\ldots

While the popularisation of forensic science has done much to
educate the public, it has also lead to many misconceptions.

*************************************************************







 is causation.  The plane
flies \emph{because} of its particular shape.  And its this
ability to determine cause that forms the cornerstone of forensic
science.  The logic is more or less the following:
\begin{enumerate}
\item If action $A$ causes some outcome $\alpha$; and
\item No other action $B$ causes $\alpha$; then
\item The presence of $\alpha$ implies that $A$ not only
happened, but was the cause of $\alpha$.
\end{enumerate}

Of course this can be generalised as follows
\begin{enumerate}
\item If any of actions $A_i\ldots A_n$ cause some outcome $\alpha$; and
\item No other action $B$ causes $\alpha$; then
\item The presence of $\alpha$ implies that not only did one or
more $A_i$ happen, but one or more $A_i$ caused $\alpha$.
\end{enumerate}


\end{document}



\section*{Preamble}
A manifesto is a document that set out the perspective of the
author of the manifesto, or that of the group on behalf of whom
the manifesto is written.  The use of the manifesto, as literary
form, dates back several centuries, and is a tool used in various
social contexts.  In contrast to most scientific publications, a
manifesto stems from a conviction, rather than objectively
verifiable facts.  To be more specific: Manifestos are often
written in a context where many facts are available (and even
obvious), but where the interpretation of those facts is
expressed as a subjective truth, with the hope that it will
sway others to accept such a perspective as accurate or valid.

The current paper is a manifesto about the nature of digital
forensic science.  However, given the fact that manifestos differ
from the typical research paper, it is necessary to make a few
remarks about manifestos in a broader context.  In particular is
it necessary to show that such manifestos (in some cases, at
least) have a place in the academic literature.

Manifestos are typically written at `crisis' moments in history,
where (in the view of the author(s), at least) some adjustment in
the current course of action is necessary.  The manifesto is
embedded in a context and is a reaction to that context, and
should be read in the context of prevailing conditions in in
terms of how it reacts to such conditions.

As a first example to illustrate these points, consider the
Russell-Einstein Manifesto compiled by Bertrand Russell and
Albert Einstein \cite{russel-einstein} in the early stages of the
Cold War.  It opens with the claim that ``we feel that scientists
should assemble in conference to appraise the perils that have
arisen as a result of the development of weapons of mass
destruction [the nucleur bomb], and to discuss a resolution in
the spirit of the appended draft.''  This manifesto is situated in the
context of science given that its intended audience is
scientists.  However, it deviates from the usual scientific
rigour found in science in many respects; it contains a claim 
``on very good authority that a bomb can now be manufactured
which will be 2,500 times as powerful as that which destroyed
Hiroshima'' --- in the typical scientific paper `good
authority' will not be sufficient basis for substantiating such a
claim.

An example of a manifesto in \emph{our} broader discipline,
consider ``The object-oriented database system manifesto''
\cite{oodbman} published in the \emph{Proceedings of the First
International Conference on Deductive and Object-Oriented
Databases} that took place in 1989.  The trigger for that
manifesto was the fact that research activity on the topic
increased without reflection on what an object-oriented database
system really is.  The authors expressed the concern that the
first product to market may define the concept if such reflection
does not occur.  Their manifesto is therefore a `prescriptive'
document --- one that favours a position, but ``a position, not
so much expecting it to be the final word as to erect a
provisional landmark to orient further debate.''  With well over
1600 citations (according to Google Scholar) at the time of
writing, the manifesto was arguably in the end more influential
than most research papers in that area were.

Another well-known manifesto in our discipline is the \emph{GNU
Manifesto} written by Richard Stallman \cite{GNU}, which details
the `crisis' caused by commercial interests that curtailed
users' freedom to share software.  It explains (and justifies)
the free software philosophy.  Though it was officially published
in a popular magazine (rather than a scholarly publication) it
too has exerted a powerful influence in academia.  A somewhat
tenuous link between the GNU Manifesto and academia exists in
that the crisis that triggered the Manifesto happened while
Stallman worked at the MIT AI Laboratory.  It is also worth
noting that several honorary doctorates were bestowed on Stallman
by various universities around the work for his work that stemmed
from the philosophy set out in the GNU Manifesto.

These examples obviously do not imply that manifestos have an
inherent value, nor that all manifestos are equal.  They do,
however, illustrate that some manifestos have a role to play in
scholarly discourse; the natural home for a manifesto is as a
statement in a public forum --- that is, in the modern conference
room.

Digital forensic science, the focus of the current manifesto, is
at a point of crisis.  As a member of the family of forensic
sciences digital forensic science deserves the same level of
scrutiny currently directed at most forensic disciplines.  Some
of the recent forensic science scandals include the formal
recognition that FBI microscopic hair analysis until 2000 was
flawed \cite{fbi-hair}, and the more recent concerns about
forensic bite-mark analysis \cite{bites}.  These concerns are not
new and not limited to a few isolated disciplines.  The most
thorough review of forensic science remains the US National
Academies of Science report on forensic science, published in
2009 \cite{nas}.  The problem caused by flawed (or unscientific
or pseudo-scientific) forensic science is twofold: On the one
hand it affords punishment to the innocent and let the guilty
escape such punishment.  Secondly, the unreliability of some
activities under the rubric of forensic science casts doubts over
forensic disciplines that are indeed scientific.  The science
epithet in forensic science if applied correctly, should be a
sign of reliability; unfortunately, in too many forensic
disciplines that epithet is used to create a sense of authority,
rather than to describe the foundations of the discipline.

The subtitle of the National Academies of Science report is ``A
way forward''.  This is also the goal of the manifesto that
follows; it is an attempt to give justifiable meaning to the
phrase \emph{digital forensic science}.  The phrase \emph{digital
forensics} is encountered more frequently that \emph{digital
forensic science}; however, the shorter phrase simply accepts
that science is implied.  Hence the assignment of meaning to
\emph{digital forensic science} in the current manifesto
automatically extends the same meaning to the contracted
\emph{digital forensics} form.


***************************************

Developments after the dark ages gave us an absolute belief in
science.  Hence science was to be a perfect tool to achieve
justice.
However, a couple of issues arose.  In postmodern times our
believe in scientific truth is no longer absolute.  Kuhn showed
us that scientific truth changes when old theories are no longer
sufficient.  From Popper we learnt that scientific theories often
are not proven --- we only need to know how to disprove them.
While these claims are not exactly what Kuhn and Popper said,
they are close enough to indicate the root of some confusion.  To
make matters worse we don't even all agree on what science is ---
to the philosophically inclined it is interesting to see how
various people read Kuhn and Popper, or, in a postmodern twist,
to see how such people are \emph{written} by Kunh and Popper (as
well as written by many other philosophers, theorists and
ignorance).  Add to this how incorrect application of `correct'
science has proven the guilt of the innocent and the innocence of
the guilty, we have --- to put it mildly --- a quagmire.

%One option is to abandon the project that employs scientific
%truth to find truth for the law --- and some people do take this
%route.  To argue that they are wrong is an arduous task and one
%that we will not attempt to undertake here.  Here we will use
%the more `pragmatic' approach that claims that it is not
%necessary to throw the baby out with the bathwater, with the full
%knowledge that this metaphor will only convince those who want to
%be convinced.  We'll have to return to this issue at some stage
%and present a more convincing argument.



**************************************



However, the understanding of knowledge is an old problem; over two
millennia ago Aristotle already proposed a classification scheme:
In his
text, \emph{The Nicomachean Ethics}, he distinguished between
\emph{episteme}, \emph{techn\'e} and \emph{phronesis} --- a
distinction that is still useful, and which will be used below to
consider the basis for making truth claims based on forensic
science.

\emph{Episteme} originates from the ancient Greek word with the
meaning ``to know''.  Prior to Aristotle's classification, Plato
already found it necessary to distinguish between
\emph{empisteme} as a ``justified true belief'', in contrast to
\emph{doxa}, which he saw as the (unjustified) popular opinion.
In today's language we associate \emph{episteme} with science.
Episteme is the stem of the word \emph{epistemology}, which is
the study of what can be known (or, of what we can indeed assert
as justified true beliefs).  Epistemology has a long history,
with hundreds of philosophers critiquing, extending, delineating
and otherwise weighing in on the topic.  Hence, claiming that
\emph{episteme} equates to science is an oversimplification, but
the alternative is to consider an informed discourse stretching
over millennia in detail.

\emph{Techn\'e} refers to technical know-how --- the arts and
crafts of today.  In fact, it's problematic to discuss the notion
of \emph{techn\'e} in English because the concept does not have a
proper equivalent in English.  In many cases in the world of
today the notions of science and \emph{techn\'e} are used in
combination to recognise that the two concepts belong together,
but are not the same.  In English this is often expressed as
``science and \emph{technology}.''  However, the English word
\emph{technology} also has other meanings leading to a conflation
of disparate concepts.

The word \emph{techn\'e} was derived from the Greek word for
\emph{art}. More specifically as a form
of knowledge \emph{techn\'e} refers to ``how to'' knowledge. 
It may, therefore
refer to, for example, knowledge about how to create a statue out
of a piece of marble. However, it also has the more technical
meaning that we see in the stem of the word \emph{artisan}.
\emph{Art} here refers to the skillset that enables someone to make
tools and other artefacts, repair a motor vehicle or cutting
someone's hair.  In the same sense \emph{techn\'e} is often
translated as craft, as one sees in the stem of the word
craftsperson.

However, neither \emph{art} nor \emph{craft} conveys the
appropriate meaning in full. (Technology and technique are two
other candidates that also do not capture the precise meaning.)

%Given the difficulty to properly express the notion of
%\emph{techn\'e} --- and the important differences between it and
%science --- another example may be useful: In the early days of
%photography the problem was one of permanently `capturing the
%light'.

Note that \emph{techn\'e} knowledge may --- but need not ---
occur in the form of applied science.  In such cases, those
applying the knowledge have a justified true belief that
applying the knowledge will lead to a predetermined outcome.
However, those using such knowledge can often achieve the desired
outcome without being able to justify their belief, apart from
`knowing' that it comes from some source they trust (and,
possibly, knowing from experience that it always works).

From the above it is clear that an expert witness using techn\'e 
knowledge as a  basis will be able to testify about his or her
observations (over time) and may even be able to testify that
they have frequently or rarely encountered a certain phenomenon
that may be present in the case being heard.


Phronesis is wisdom or, more precisely, practical wisdom.  It
is knowledge enabling one to make the `right' decisions and
choices, or making `wise' decisions and choices.  It is practical
in the sense that it is situated in a given context.  In the
legal arena, this is arguably the type of knowledge the judge
needs.  Forensic scientists need this type of knowledge when
faced by moral dilemmas.  Phronesis is not absolute and do not
claim objective factual knowledge.  So, while it should form part
of any forensic scientist's knowledge, this is not what the
forensic scientist will testify to.  It is not rooted in science;
if related to science it arguably transcends science as a higher,
but essentially unknowable knowledge.

*********************************************

The discussion above solves one dilemma (characterising the type
of evidence to be reasonably expected from an expert in terms of
the knowledge the expert uses) by introducing another problem:
determining the type of knowledge the expert uses.
In all three categories above  experts typically use widely
accepted methods based on a common extensive body of knowledge.
In all cases there may be differences of opinion on the best
method to use, as well as about some details.  There may be
disagreement about whether a specific ``law of nature'' has been
proven. In digital forensics there may even be a further debate
about what form such laws of nature may assume (if they exist at
all).

The questions raised in the previous paragraph are not unique to
forensic science (or even digital forensic science), but have
been raised continually over the past few centuries.  In its
general form the problem even has a name: the demarcation
problem.  A discussion of the demarcation problem will, however,
have to wait until later.

*********************************************

In the Wells Harbour Case the judge found Smeaton's testimony
useful and admitted it.  Since English courts used (and still
use) common law, this acceptance of science caused a legal
precedent that opened the doors for subsequent scientific
testimony.  Over time testimony based on science found its way
into most legal systems to the extent that forensic science may
be seen as a universal phenomenon (with some local differences
about exactly what is considered scientific --- or scientific
enough --- to be acceptable for testimonial use).

***************************************************

There are, at least, three reasons why a belief may exist that
something is scientifically proven.  Firstly, science often
posits new theories that initially seem valuable, but are
eventually refuted.  Secondly, it is hard for non-specialists to
distinguish between science and pseudo-science --- that is,
activities that imitate science but do not apply scientific
rigour to the process.  Finally, the legal system may have
accepted a theory as true and then treat it as a legal truth
despite concerns expressed in the scientific community about the
soundness of the theory.  It is, for example, worth reflecting on the
scientific validity of results obtained via a polygraph.  More
recent examples for such reflection include microscopic hair
analysis and bitemark analysis.

The term \emph{knowledge} refers to a set of claims that are
true.  In one direction knowledge may be qualified by who
possesses that knowledge: human knowledge may refer to the set of
knowledge that the human race has collected; expert knowledge may
refer to a set of claims that an expert holds in some specific
area; common sense may refer to knowledge that every human being
is assumed to have.


*******************************************************

Aristotle classified knowledge millennia ago in three categories;
this classification is still useful.  A crude modern-day
interpretation of these categories would be scientific knowledge,
how-to knowledge and wisdom.  All three categories of knowledge
may be useful to the arbitrator of a legal dispute.  Note that
such an arbitrator typically needs wisdom, and restrict
discussion of evidence to the other two categories.

Scientific knowledge is (at least in its ideal form) objective
--- in other words, it is independent of the individual scientist and,
in the legal context, independent from the witness who testifies
about scientific facts.  A scientific finding that two DNA
samples originated from the same donor should not depend on the
scientist who performed the test.  (It is possible that a
scientist did not follow the correct procedure and that the
outcome of the test therefore questionable, but two scientists
who perform the same test correctly are expected to arrive at the
same result.)

As a simple example from computing consider the case where a
computer scientist demonstrates that solving a certain problem
$P$ is equivalent to solving the halting problem and concludes
that the problem $P$ cannot be solved by a computer.  If the work
is sound, the \emph{conclusion} that $P$ is unsolvable is an
objective fact.  The scientist can explain this finding based on
the fact that the halting problem has been proven to be
undecidable.  This proof rests on the Church-Turing thesis --- a
conjecture that has not been proven.  However, it is a thesis
that is formulated as a falsifiable claim in line Popper's widely
accepted falsifiable claims as the demarcation criterion between
science and non-science.

The second category of knowledge was informally labelled as
how-to knowledge.  People working as programmers, engineers, wine
makers, rugby coaches, musicians, medical doctors and street
sweepers use this category of knowledge for their daily jobs.
The how-to knowledge may or may not be based on scientific
knowledge.  The programmer may, for example, just tinker with
code.  Or the programmer way choose to use quicksort rather than
bubble sort based on (scientific) knowledge of the time and space
complexity of the two algorithms.  The programmer may even build
a compiler or operating system based on the theory of building
such systems that emerged from extensive research about such
systems.  However, such a programmer rarely works as a scientist:
her or she rarely use the ``scientific method'' (even when one
caters for the inherent ambiguity of the concept); this
programmer rarely tests scientific theories or produce new
theories; this programmer rarely publishes new scientific
theories or truths in scholarly journals.  The practitioner who
uses how-to knowledge typically is faced by complex situations
where choices have to be made based on incomplete (or even
contradictory) information.  The practitioner develops expertise
over time, but, where appropriate adjusts such experience based
on new scientific or technical developments.  It is entirely
possible that two practitioners when faced with an identical
complex situation may reach different conclusions and proceed in
a different manner, where either conclusion or choice about how
to proceed can be justified.  Since the (specific) experience of
the expert may affect the decision or conclusion of the expert,
objectivity is not guaranteed.

Stated differently, useful experts come in different forms.  The
nature of the truths they are allowed to testify to, depend on the
nature of their expert knowledge.  Forensic science is explicitly
rooted in science and, hence, forensic scientists are (in principle) 
allowed to testify
about their `justified true beliefs' based on their scientific
conclusions.  This is also true when \emph{forensic} or
\emph{forensics} is used to denote \emph{forensic science}.  Note
that the claim above that the forensic expert is (in principle)
allowed to testify about justified true beliefs had to be
qualified, because the final decision about admission of evidence
is to be made by the court.  That is also the reason for opening
this paragraph with a reference to \emph{useful} experts: there
are many reasons why a court may decide not to accept an expert's
claimed expertise (including claimed scientific expertise) as
useful.

A final note to conclude this section: note that the type of
testimony depends on the nature of knowledge used: the same
expert may use scientific and how-to knowledge, but has to
distinguish between the two in terms of testimony provided.  This
will also have to wait to be discussed later.


