In an era where the veracity of visual and audio evidence has
historically been taken for granted, the rise of deepfakes presents
a critical challenge. Here, we explore the impact of seemingly
undetectable deepfakes on society, investigations, and disputes,
examining how the prevailing trust in what people see or hear can
be – and increasingly is – exploited.
Deepfakes: The emergence of synthetic media
Deepfakes are a type of synthetic media that use Artificial
Intelligence (AI) to create realistic images, videos, or audio
recordings that are not real. The term “deepfake” comes
from the blending of “deep learning” and “fake”
into the now infamous portmanteau.
Continued advancements in AI technology have ushered in a new
era of deepfakes, where the creation of convincingly manipulated
media has become increasingly accessible. With minimal technical
expertise and just a basic understanding of the appropriate
software, individuals can now generate deepfakes with relative
ease. Often this software is available for free or at minimal cost,
and there are many videos available on popular video-sharing
platforms showing how to get started.
While amateur attempts at generating deepfakes may still exhibit
noticeable flaws, the rapid progress in AI technology raises the
alarming possibility that these fabrications could soon become
indistinguishable from genuine articles. Combining the deepfake
technology with a convincing narrative can dupe people into
believing what they are watching is real, as a relatively recent
deepfake video posted to Facebook demonstrated.
The technology behind deepfakes: How machine learning
algorithms create convincing counterfeits
Deepfakes rely on machine learning algorithms, specifically deep
neural networks, to analyse and manipulate existing images, videos,
or audio recordings. These algorithms learn to mimic the behaviour
of a person or object and then apply those learned patterns to the
creation of a highly realistic counterfeit.
Creating persuasive deepfakes necessitates a significant volume
of training data, typically consisting of numerous images or videos
featuring the targeted individual. By utilising this data as a
reference, deep learning algorithms can discern and capture the
distinctive attributes and facial expressions specific to the
person being impersonated. Continuous refinement and incorporation
of feedback by the creators plays a vital role in improving the
quality and authenticity of the deepfake. Although deepfakes are
commonly associated with image and video manipulation, parallel
techniques can be applied to audio, enabling the production of
manipulated voice recordings or synthesised speech.
Realism in deepfakes is achieved through the utilisation of
Generative Adversarial Networks (GANs), a technique that involves
generating new images based on source data and subjecting them to
ongoing evaluation. This iterative process of
“discrimination” rejects results that fail to meet
certain criteria, allowing for the continual enhancement of the
output quality. As the generation and discrimination cycles
persist, the deepfake gradually approaches a point where it becomes
virtually indistinguishable from authentic media.
The technology enabling their creation is advancing rapidly,
with increasingly advanced open-source AI tools available allowing
generation of deepfakes for free, as reported
by the New York Times.
Spotting the unseen: Identifying deepfakes in the age of
AI
With significant advancements in this area, even shallow fakes
are too good to spot on a cursory examination. However, there are a
few basic methods that may be employed to identify deepfakes and,
to some extent, prevent them from being shared further. For
instance, one can evaluate the video’s lighting and shadows,
search for inconsistent motion or facial expressions, and listen
for oddities in the audio. Examination of the face and how the
person blinks is a good place to start, since high-end deepfake
manipulations almost always involve facial alterations. Observing any mismatch or
lack of synchronisation between the movements of the lips and the
words being spoken could serve as a clue.
Aside from just relying on our senses to detect deepfakes, there
are also improvements being made in deepfake detection algorithms,
such as the one developed by Stanford University, which essentially uses AI to detect
deepfakes created by AI. However, as some digital-forensics experts estimate,
people working on video synthesis outnumber those working on
detection 100 to 1. A recent evaluation of Intel’s
“FakeCatcher” by BBC News showed a less-than-perfect result,
even identifying some authentic videos as fake.
The dual nature of deepfakes: Societal risks and
opportunities
Some experts estimate that as much as 90% of online audio-visual content
could be synthetically generated within the next few years. This
“unchecked” rise of deepfakes could have wider societal impacts, such as eroding trust in
media and institutions. People may no longer have a “shared
reality” and may revert to only trusting what they have seen
(or people that they know and trust have seen). The World Economic Forum’s recently published
report also sheds lights on some specific conduct risks presented
by deepfakes. These might range from financial fraud using
impersonation, to social media catfishing which involves exploiting
people for money or gifts, to electoral manipulation by swaying
public opinion using doctored videos of political figures. This was
exemplified in a recent report on a state-aligned campaign, where deepfakes were
used to spread misinformation using deceptive political
content.
On the flip side, there are also potential pro-social
applications of deepfakes, such as creating more realistic virtual
experiences or improving accessibility for people with
disabilities. For example, a British start-up company has been developing a retail app that would let users
upload videos of their faces and create deepfake outputs in
minutes, substituting the model with the user. In medicine, Project Revoice
was launched to help people with Amyotrophic Lateral Sclerosis
(ALS) regain their voices that they lost to the disease. While a
few years ago, it was state-of-the-art technology to hear the
renowned physicist, Stephen Hawking “speak” in his
robotic, computer-generated voice, today’s initiatives such as
Revoice can help restore a patient’s natural voice.
Overall, the impact of deepfakes on society is complex and
multifaceted, but one fact that cannot be denied is its dual
nature, in that deepfakes will usher in new opportunities, as well
as new dangers. It is therefore critical to ensure people know when
a photo or video is generated by AI, as emphasised by
Microsoft’s president, Brad Smith, in a recent speech in Washington.
Deepfakes in the Courts: Impact in the world of investigations
and disputes
Deepfakes have the potential to impact the world of
investigations and disputes by creating a digital fact base that
can be subject to fakes. When the line between real and imitation
becomes blurred, the consequences of false accusations, incorrect
judgements, and an erosion of trust in our judicial system are all
too real.
In criminal cases, there is a rebuttable presumption that computers
operate correctly in producing electronic evidence. However,
there are multiple cases and investigations which exist
as evidence against this presumption, even without malintent of the
involved parties.
Audio-visual material isn’t viewed in the same light as
other computer outputs such as reports. We can all understand the
risk of nefarious doctoring of an accounting report in a corporate
fraud investigation, but it becomes much trickier when our eyes and
ears are being deceived.
As individuals increasingly lack the ability to spot
deepfakes, detection algorithms will be required to
consistently and accurately flag faked evidence. In the same way
that data experts are required to provide expert testimony on the
sanctity of data, digital forensic experts will be required to
validate the authenticity of audio-visual material using these
detection algorithms. Aside from the increased time and expenditure
in legal proceedings, the most troublesome aspect could prove to be
the cat-and-mouse game between the good and the bad players in
leveraging technological development.
While we continue to see significant breakthroughs in the
development of deepfake technology, there is a real risk of
detection algorithms lagging for extended periods of time. Such
exposure calls into question the reliance that can be placed on
audio-visual evidence, if any. Sufficient doubt can be raised by
defendants submitting a “deepfake defence“, claiming that any
audio-visual material is not authentic. For the claimant, the
burden of proof then becomes one of proving a negative, i.e., that
the material is not fake, which may be legally impossible.
It is likely that many investigations and/or disputes concluded
on the basis of audio-visual evidence will quickly be appealed once
detection technology makes its next significant leap. How many
times this happens before an equilibrium is reached is unclear, but
what is clear is the negative impact on judicial proceedings if
allowed to subsist.
Looking ahead: The rise of a zero-trust society?
Use of the term “fake news”, which the former US
President wrongly claimed to have first coined, sought
to undermine trust in modern media by casting doubt on the honesty
of reporters and journalists. Deepfakes have the potential to
amplify this effect and create a zero-trust society where people
cannot, or are unwilling to, distinguish between the real and the
fake.
Aside from the evident issue of faked realities, there exists
the issue of real realities becoming plausibly deniable. We are
already at the stage where individuals are easily duped by
deepfakes, and detection algorithms are struggling to keep pace
with the developing technology. If distinction proves unreliable,
or the associated time and costs are impractical, the default
defence will be to cry “deepfake”. Shocking footage which
showed extrajudicial executions by Cameroon military
personnel was initially dismissed by the Ministry of
Communication as being fake. An investigation by Amnesty
International experts gathered extensive and credible evidence that
suggests the footage is genuine. There are numerous other such
examples, and they are likely to continue while denial remains
plausible.
This obfuscation of reality in both directions will have a
profound and far-reaching impact on investigations and disputes,
affecting politics, journalism, and legal proceedings.
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