Facing reality?
Law enforcement
and the challenge
of deepfakes
An Observatory Report from the Europol Innovation Lab

FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES
An Observatory Report from the Europol Innovation Lab
 
Neither the European Union Agency for Law Enforcement Cooperation nor any person acting
on behalf of the agency is responsible for the use that might be made of the following information.
Luxembourg: Publications Office of the European Union, 2022
PDF | ISBN 978-92-95236-23-3 | DOI: 10.2813/158794 | QL-02-24-129-EN-N
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Cite this publication: Europol (2022), Facing reality? Law enforcement and the challenge
of deepfakes, an observatory report from the Europol Innovation Lab, Publications Office
of the European Union, Luxembourg.
This version published in January 2024 replaces the previous one. Updates were made
in the chapter Understanding deepfakes.
This publication and more information on Europol are available on the Internet.
www.europol.europa.eu

Contents
4 Introduction
5 Understanding deepfakes
7 The technology behind deepfakes
Deep learning
Generative Adversarial Networks (GAN)
10 Deepfake technology’s
impact on crime
Disinformation
Document fraud
Deepfake as a service
14 Deepfake technology’s impact
on law enforcement
Impact on police work
Impact on the legal process
New capacities needed
16 Deepfake detection
Manual detection
Automated detection
Preventive measures
19 How are other actors
responding to deepfakes?
Technology companies
European Union
21 Conclusion
3

Today, threat actors are using disinformation campaigns and
deepfake content to misinform the public about events, to influence
politics and elections, to contribute to fraud, and to manipulate
shareholders in a corporate context. Many organisations have
now begun to see deepfakes as an even bigger potential risk than
identity theft (for which deepfakes can also be used), especially
now that most interactions have moved online since the COVID-19
pandemic. This concern is echoed by a recent report by University
College London (UCL) that ranks deepfake technology as one of the
biggest threats faced by society today.1
 
This poses a risk to EU citizens. Europol, as the criminal information
hub for law enforcement organisations, will continue to play its part
in supporting law enforcement authorities in the EU Member States
to counter this threat.
This report presents the first published analysis of the Europol
Innovation Lab’s Observatory function, focusing on deepfakes,
the technology behind them and their potential impact on law
enforcement and EU citizens. Deepfake technology uses Artificial
Intelligence to audio and audio-visual content. Deepfake technology
can produce content that convincingly shows people saying or
doing things they never did, or create personas that never existed
in the first place.
To date, the Europol Innovation Lab has organised three strategic
foresight activities with EU Member State law enforcement
agencies and other experts. During strategic foresight activities
conducted by the Europol Innovation Lab, over 80 law enforcement
experts identified and analysed the trends and technologies they
believed would impact their work until 2030. These sessions
showed that one of the most worrying technological trends is the
evolution and detection of deepfakes, as well as the need to address
disinformation more generally. The findings in this report are the
result of extensive desk research supported by research provided
by partner organisations, expert consultation, and the strategic
foresight activities.
Those workshops provided the initial input for this report.
Furthermore, the findings are the result of extensive desk research
supported by research provided by partner organisations, expert
consultation and the strategic foresight activities conducted by
the Europol Innovation Lab.
Strategic foresight and scenario methods offer a way to understand
and prepare for the potential impact of new technologies on law
enforcement. The Europol Innovation Lab’s Observatory function
monitors technological developments that are relevant for law
enforcement and reports on the risks, threats and opportunities of
these emerging technologies.
1  UCL – London’s Global University, ‘‘Deepfakes’ ranked as most serious AI crime threat’,
https://www.ucl.ac.uk/news/2020/aug/deepfakes-ranked-most-serious-ai-crime-threat.
Introduction
4FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

Disinformation is being spread with the intention to deceive. Tools of
disinformation campaigns can include deepfakes, falsified photos,
counterfeit websites and other information taken out of context to
deceive the audience.2
In the original, strict sense, deepfakes are a type of synthetic media
mostly disseminated with malicious intent, although they are now
often used for positive applications too.3
 Synthetic media refers
to media generated or manipulated using artificial intelligence
(AI). In most cases, synthetic media is generated for gaming, to
improve services or to improve the quality of life, but the increase
in synthetic media and improved technology has given rise to
disinformation possibilities, including deepfakes.
Deepfakes were examined and discussed at great length in one
of the Europol Innovation Lab’s strategic foresight activities.
Law enforcement experts who participated in these activities
expressed concern about the consequences of disinformation,
fake news and social media on political and social discourse.
These trends are expected to become more pronounced as the
supporting technologies, such as deepfakes, are becoming more
sophisticated. Their impact on privacy and personal security
will doubtless result in new categories of crime that will have to
be policed. Participants were especially concerned about the
weaponisation of social media and the impact of misinformation
on public discourse and social cohesion.
On a daily basis, people trust their own perception to guide them
and tell them what is real and what is not. This applies not only
to people in their private lives, but also law enforcement officers
trying to do their jobs. First-hand accounts are valued higher than
second-hand versions of an event. Auditory and visual recordings
of an event are often treated as a truthful account of an event.
Photographs and videos are important intelligence for police work
and evidence in court. But what if these media can be generated
artificially, adapted to show events that never took place, to
misrepresent events, or to distort the truth?
For instance, prior to the invasion of Ukraine by Russia in 2022,
the United States revealed a Russian plot to use deepfake video
to justify an invasion of Ukraine.4
 After the invasion happened,
officials of the Ukrainian government warned that Russia might
spread deepfakes that will show the Ukrainian president Volodymyr
2  Die Bundesregierung, ‘What is disinformation?’, accessed 15 March 2022, https://www.
bundesregierung.de/breg-de/themen/umgang-mit-desinformation/disinformation-definition-1911048 .
3  ENLETS, ‘SYNTHETIC REALITY & DEEP FAKES: IMPACT ON POLICE WORK’, 2021, accessed
on 15 March 2022, https://enlets.eu/wp-content/uploads/2021/11/Final-Synthetic-Reality-Deep-fakes-Impact-on-Police-Work-04.11.21.pdf.
4 CBS News, ‘U.S. reveals Russian plot to use fake video as pretense for Ukraine invasion’, 2022,
accessed on 10 March 2022, https://www.cbsnews.com/news/russia-disinformation-video-ukraine-invasion-united-states/.
Understanding
deepfakes
5

Zelenskyy surrendering.5
 This fear appears to have become reality
after hackers made a Ukrainian news website show a video of
president Zelenskyy telling his soldiers to surrender.6
 At the time
of writing much is still unclear about the video and it has not been
verified to be a real deepfake or another fake, but it does show how
the use of (deep)fakes are being used for disinformation purposes.
Examples like the one above show that this type of disinformation
can be dangerous. Its aim is to intensify existing conflicts and
debates, undermine trust in state-run institutions and stir up anger
and emotions in general. The erosion of trust is likely to make the
business of policing harder.
This challenge to policing is coupled with a public that seems
relatively uninformed about the dangers of deepfakes. Despite their
increasing prevalence at the time, research in 2019 showed almost
72% of people in a UK survey to be unaware of deepfakes and their
impact.7
 This is particularly worrying as people might be unable to
identify deepfakes (videos, photos, audios) since they are not aware
of the existence of such virtual forgeries or how they work. The lack
of understanding of the basics of this technology presents various
challenges, some of which are relevant for law enforcement (such
as disinformation and document fraud). Even more worrying results
from recent experiments have shown that increasing awareness
of deepfakes may not improve the chances for people to detect
them.8
 Researchers are therefore expecting criminals to increase
their use of deepfakes in the coming years.9
 This shows it is vital to
understand the deepfake threat and prepare ourselves.
5 Metro, ‘Ukraine warns Russia may deploy deepfakes of Volodmyr Zelensky surrendering’,
2021, accessed on 15 March 2022, https://metro.co.uk/2022/03/04/ukraine-warns-russia-may-deploy-deepfakes-of-zelensky-surrendering-16217350.
6  National Public Radio, ‘Deepfake video of Zelenskyy could be ‘tip of the iceberg’
in info war, experts warn’, 2021, accessed on 17 March 2022, https://www.npr.
org/2022/03/16/1087062648/deepfake-video-zelenskyy-experts-war-manipulation-ukraine-russia.
7 iProov, ‘Almost Three-Quarters of UK Public Unaware of Deepfake Threat, New Research’,
2019, accessed 15 March 2022, https://www.iproov.com/press/uk-public-deepfake-threat.
8 Köbis, N.C. et al., ‘Fooled twice: People cannot detect deepfakes but think they can’, iScience,
24(11), 2021, accessed 15 March 2022, https://doi.org/10.1016/j.isci.2021.103364.
9 Recorded Future, Insikt Group, ‘The Business of Fraud: Deepfakes, Fraud’s Next Frontier’,
2021.
6FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

Deepfake technology uses the power of deep learning technology
to audio and audio-visual content. Employed properly, these models
can produce content that convincingly shows people saying or
doing things they never did, or create people that never existed
in the first place. The rise of the application of AI to generate
deepfakes is already having, and will have, further implications for
the way people treat recorded media. Here we discuss two core
advancements behind deepfake technology, namely deep learning
and generative adversarial networks, and how 5G technology may
further enable the use of deepfakes.
Deep learning
Deep learning is a kind of machine learning where a computer
analyses datasets to look for patterns with the help of neural
networks. Machine learning is an application of AI where
computers automatically improve through the use of data.
Deep learning is a kind of machine learning that applies neural
networks. These neural networks mimic the way our brains work
to more effectively learn from the data provided. Deep learning
technology, paired with the availability of large databases with
material to train the generative models on, has allowed for rapid
improvement of deepfake technology.
Deep learning algorithms use neural networks that mimic the brain’s
processes to find patterns in data.10 Therefore, the availability of
data is essential for a good deepfake system; it needs examples to
learn what the result has to look like. It will try to discover patterns
in the available data and thus extract what features are important
and how these relate to each other. That will allow it to construct a
complete and convincing picture. Depending on the quality of the
available data and the factors the algorithm uses, the result may be
more or less realistic.
Today, large datasets with labelled visual material are becoming
freely available on the internet. These datasets are essential for
the training of the machine learning algorithms needed to produce
deepfakes. Creators of deepfakes can use these freely available
datasets on the internet and avoid the time-consuming work of
creating datasets themselves.
10 Code Academy, ‘What Is Deep Learning?’, 2021, accessed on 10 March 2022, https://www.
codecademy.com/resources/blog/what-is-deep-learning/.
The technology
behind deepfakes
7

In one example from 2018, filmmaker Jordan
Peele and BuzzFeed CEO Jordan Peretti
created a deepfake video to warn the public
about disinformation, specifically regarding
the public’s perception of political leaders.
Peele and Peretti used free tools with the help
of editing experts to overlay Peele’s voice and
mouth over a pre-existing video of Barack
Obama. In the video, Obama allegedly said,
“We are entering an era in which our enemies
can make it look like anyone is saying anything,
at any point in time. Even if they would never say
those things.”11
Source: Suwajanakorn, S. et al., 2017, ‘Synthesizing Obama: learning lip
sync from audio’, ACM Transactions on Graphics, 36(4), accessed on 15
March 2022, https://dl.acm.org/doi/10.1145/3072959.3073640.
Generative Adversarial Networks (GAN)
A great leap in the quality and accessibility of deepfake technology
was made by the adaptation of generative adversarial networks
(GANs) as proposed in 2014 by Ian Goodfellow et al.12 A GAN works
with two competing models: a generative and a discriminating
model. The generative model creates content based on the available
training data, trying to capture the data as closely as possible,
to create content that most closely mimics the examples in the
training data. A discriminative model then tests the results of the
generative model by assessing the probability the tested sample
comes from the dataset rather than the generative model.
With the results from these tests, the models continuously improve
until the generated content is just as likely to come from the
generative model as the training data. This powerful method both
simplifies the learning process, making it more accessible, and also
improves the outcome by incorporating a mechanism designed
to minimise the chance its product would be discriminated from
authentic content.
When a new feature that may help discriminate between synthetic
and authentic content is discovered, it allows for an easy
11 Ars Electronica, ‘Obama Deep Fake’, 2018, accessed on 10 March 2022, https://ars.
electronica.art/center/en/obama-deep-fake/.
12 Goodfellow, I. et al, (2014), Generative Adversarial Nets (PDF). Proceedings of the
International Conference on Neural Information Processing Systems (NIPS 2014). pp.
2672–2680. 
8FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

incorporation of that feature. For example, people’s eyes would
not blink in early deepfake videos, making them relatively easy
to detect.13 Even though the training data for deepfake models
included many pictures of people, these people generally did
not blink in pictures. Adding more videos with people blinking to
the database allowed both models to work together to produce
people with blinking eyes, making the result more realistic and
consequently harder to differentiate from authentic content.
Training data to create deepfakes may be applied in various ways
for video and image deepfakes:
Face swap
Transfer the face of one person for that of the person in the video;
Attribute editing
Change characteristics of the person in the video, e.g. style or
colour of the hair;
Face re-enactment
Transferring the facial expressions from the face of one person
onto the person in the target video;
Fully synthetic material
Real material is used to train what people look like,
but the resulting picture is entirely made up.
See for example https://www.thispersondoesnotexist.com
and https://generated.photos
Optimising these factors will improve the outcome. The more
extensive the database and the more complex the algorithm
becomes, the more computing power is necessary. Generating
quality data requires a large volume and diversity of data with
enough examples of similar but slightly different representations of
the same characteristics to work. For example, if a database mostly
contains pictures of white men with black hair, it will not perform
too well on creating Asian women with blonde hair. As an increasing
number and volume of databases are available, the quality and
quantity of training data increases. This has allowed the models
generating deepfakes to increase in sophistication.
Participants in the Innovation Lab’s foresight activities noted how
the roll-out of 5G would enhance connectivity and communication
within law enforcement agencies (LEAs) and would strengthen
the privacy and security of organisations and individuals alike.
However, they noted that those same benefits would be leveraged
by criminals to perpetrate their crimes. The additional bandwidth
offered by new communication technologies, such as 5G, enables
users to utilise the power of cloud computing to manipulate video
streams in real time. Deepfake technologies can therefore be
applied in videoconferencing settings, live-streaming video services
and television.
13 GIZMODO, ‘Most Deepfake Videos Have One Glaring Flaw’, 2018, accessed on 10 March
2022, https://gizmodo.com/most-deepfake-videos-have-one-glaring-flaw-1826869949.
9

Participants of the foresight activities cited several trends that
European LEAs should be sensitive to. Of note is crime as a service
(CaaS), with criminals selling access to the tools, technologies and
knowledge to facilitate cyber and cyber-enabled crime. CaaS is
expected to evolve in parallel with current technologies, resulting in
the automation of crimes such as hacking and adversarial machine
learning and deepfakes. Indeed, participants flagged the tendency
of criminal actors to become early adopters of new technologies.
As a result, they are always one step ahead of law enforcement in
their implementation, use and adaptation of these technologies.
The growing availability of disinformation and deepfakes will have
a profound impact on the way people perceive authority and
information media. With the increasing volume of deepfakes, trust
in authorities and official facts is undermined. Experts fear this may
lead to a situation where citizens no longer have a shared reality, or
could create societal confusion about which information sources
are reliable; a situation sometimes referred to as ‘information
apocalypse’ or ‘reality apathy’.14
This makes it essential to be aware of this manipulation and
be prepared to deal with the phenomenon, so as to distinguish
between benign and malicious use of this technology.
The ‘Malicious Uses and Abuses of Artificial Intelligence’ report
by Europol, TrendMicro and UNICRI15 included a case study on
this topic.
The report also shows that deepfake technology can facilitate
various criminal activities, including:
14  The Guardian, 2018, accessed on 10 March 2022, ‘An information apocalypse is coming.
How can we protect ourselves?’, https://www.theguardian.com/commentisfree/2018/
mar/16/an-information-apocalypse-is-coming-how-can-we-protect-ourselves.
15 Europol, ‘Malicious Uses and Abuses of Artificial Intelligence’, 2020, accessed on 10 March
2022, https://www.europol.europa.eu/publications-events/publications/malicious-uses-and-abuses-of-artificial-intelligence.
16 KYC stands for Know Your Customer and refers to the processes for identity verification and
fraud risk assessment used by institutions.
• harassing or humiliating
individuals online;
• perpetrating extortion
and fraud;
• facilitating document fraud;
• falsifying online identities and
fooling ‘know your customer’
mechanisms16;
• non-consensual pornography;
• online child sexual
exploitation;
• falsifying or manipulating
electronic evidence for
criminal justice investigations;
• disrupting financial markets;
• distributing disinformation
and manipulating public
opinion;
• supporting the narratives of
extremist or terrorist groups;
• stoking social unrest and
political polarisation.
Deepfake
technology’s
impact on crime
10FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

Disinformation
Disinformation campaigns are operations to deliberately spread
false information in order to deceive.17 One major concern about
this use is the ease of creating a fake emergency alert that warns of
an impending attack. Another concern is the disruption of elections
or other aspects of politics by releasing a fake audio or video
recording of a candidate or other political figure. To illustrate this,
the BBC created a video for the 2019 general election in the UK in
which the candidates Boris Johnson and Jeremy Corbyn endorsed
each other.18 If this kind of manipulation successfully deceives a
large enough part of the populace, this could have a serious impact
on the outcome of an election.
Businesses are also at risk of being targets of disinformation, as
deepfakes can be used to generate false information that could
fool the public. For example, a threat actor could create a deepfake
that makes it appear that a company’s executive engaged in a
controversial or illegal act. Certain deepfakes could be used for
false advertising and disinformation, which could lead to bad
publicity for a targeted company. Such applications of deepfakes
could impact areas like stock market and company value as the
public (stakeholders and shareholders, as well as consumers)
may believe the deepfake and start selling their stocks or
boycotting the company.
One example that shows the potential for criminal activities
supported by deepfakes is the case where criminals used deepfake
audio to impersonate the CEO of a company to make an employee
transfer USD 35 million.19 In this chapter/section of the report,
we will look closer at four of the criminal uses of deepfakes that
participants in the foresight activities identified.
Non-consensual pornography
In a December 2020 study, Sensity, an Amsterdam-based company
that detects and tracks deepfakes online, found 85 047 deepfake
videos on popular streaming websites, with the number doubling
every 6 months.20 In a previous September 2019 study, Sensity
discovered that 96 % of the fake videos involved non-consensual
pornography. To create this, one will overlay a victim’s face onto
the body of a pornography actor, making it appear that the victim is
engaging in the act. In many situations, the victims of pornographic
deepfakes are celebrities or high-profile individuals.
17 Marriam-Webster, ‘Disinformation’, accessed on 10 March 2022, https://www.merriam-webster.com/dictionary/disinformation
18 BBC News, ‘The fake video where Johnson and Corbyn endorse each other’, 2019, accessed
on 10 March 2022, https://www.bbc.com/news/av/technology-50381728.
19 Forbes, ‘Fraudsters Cloned Company Director’s Voice In $35 Million Bank Heist,
Police Find’, 2021, accessed on 16 March 2022, https://www.forbes.com/sites/
thomasbrewster/2021/10/14/huge-bank-fraud-uses-deep-fake-voice-tech-to-steal-millions.
20 Sensity, ‘How to Detect a Deepfake Online: Image Forensics and Analysis of Deepfake
Videos’, 2021, accessed on 10 March 2022, https://sensity.ai/blog/deepfake-detection/how-to-detect-a-deepfake/.
11

These videos are popular, having received approximately 134 million
views at the time21, and there are several pornographic sites that
specifically produce pornographic celebrity deepfakes. Perpetrators
often act anonymously, making crime attribution more difficult.
Document fraud
Passports are becoming increasingly hard to forge with modern
fraud prevention measures. Synthetic media and digitally
manipulated facial images present a new approach for document
fraud. Using different methods and tools, it is possible to combine,
or morph, the faces of the person the passport actually belongs
to and the person(s) wanting to obtain a passport illegally. This
method may increase the chance that the photo in a forged
document passes any identity checks including those using
automated means (facial recognition systems).22
The face in the middle of the image above is an example of a digitally manipulated
facial image made using this ‘morphing’ method from the other two images.
The images on the left and right are from The SiblingsDB, which contains different
datasets depicting images of individuals related by sibling relationships.
The subjects are voluntary students and employees of the Politecnico di Torino
and their siblings, in the age range between 13 and 50.23
21 Government Technology, ‘Deepfakes Are on the Rise — How Should Government Respond?’,
2020, accessed on 10 March 2022, https://www.govtech.com/policy/deepfakes-are-on-the-rise-how-should-government-respond.html.
22 Robertson, D.J., Mungall, A., Watson, D.G. et al, ‘Detecting morphed passport photos: a
training and individual differences approach,’ Cogn. Research 3, 27, 2018, accessed on 16
August 2021, https://doi.org/10.1186/s41235-018-0113-8.
MIT Technology Review, ‘The hack that could make face recognition think someone
else is you’, 2020, accessed on 10 March 2022, https://www.technologyreview.
com/2020/08/05/1006008/ai-face-recognition-hack-misidentifies-person.
Pikoulis, E.-V. et al., ‘Face Morphing, a Modern Threat to Border Security: Recent Advances
and Open Challenges’, Applied Sciences, 2021, accessed 17 February 2022, at https://www.
mdpi.com/2076-3417/11/7/3207.
23 T.F. Vieira, A. Bottino, A. Laurentini, M. De Simone, ‘Detecting Siblings in Image Pairs’, The
Visual Computer, 2014, vol 30, issue 12, p. 1333-1345, doi: 10.1007/s00371-013-0884-3
12FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

This kind of approach to fraud can be applied to any other type of
digital identity check that requires visual authentication. It greatly
undermines identity verification procedures since there is no reliable
way to detect this kind of attack.24, 25, 26, 27
Document fraud is a facilitator of other crimes like illegal
immigration, trafficking in human beings, selling of various illegal
goods, and terrorism, as perpetrators often use fake IDs to travel to
their target locations. Deepfake technology might amplify the risk
for advanced document fraud by organised crime groups.
In practice, the robustness of any identification process will depend
on the process as a whole, and not only its visual step(s). However,
a higher quality synthetic image will make a forged document more
likely to pass the check of a visual identification step in the process.
In general, the prospect of a successful document fraud attempt
depends on quality and context of the deepfake used. The quality of
the deepfake is largely dependent on available data and processing
power, which is beyond the control of the identification process. The
context in which the deepfake is applied is partially determined by
the process however, providing opportunities to limit the success of
just using a good deepfake.
Deepfake as a service
Just like many other new technologies, deepfakes are still used
mainly by proficient engineers and research parties. However,
deepfake capabilities are becoming more accessible for the
masses through deepfake apps and websites. There are special
marketplaces on which users or potential buyers can post requests
for deepfake videos (for example, requests for non-consensual
pornography). The increased demand for deepfakes has also
led to the creation of several companies that deliver deepfakes
as a product or even online service. Recorded Future has reported
a threat actor’s willingness to pay USD 16 000 for this kind
of service.28
Since deepfakes are based on advanced AI and machine learning
technologies, a high level of expertise is required to put the
technology together. Accordingly, there are not as many threat
actors with the skillset to develop them on their own as there are
24 University of Lincoln ScienceDaily, ‘Two fraudsters, one passport: Computers more accurate
than humans at detecting fraudulent identity photos,’ 2019, accessed on 20 July, 2020, at
www.sciencedaily.com/releases/2019/08/190801104038.htm.
25 Naser Damer, PhD. (n.d.). Fraunhofer IGD, ‘Face morphing: a new threat?’ accessed on 20
July 2020, at https://www.igd.fraunhofer.de/en/press/annual-reports/2018/face-morphing-a-new-threat.
26 David J. Robertson, et al. ‘Detecting morphed passport photos: a training and individual
differences approach,’ Springer Nature, 2018, accessed on 20 July 2020, at https://
cognitiveresearchjournal.springeropen.com/articles/10.1186/s41235-018-0113-8 .
27 Robin S.S. Kramer, et al., ‘Face morphing attacks: Investigating detection with humans and
Computers, Springer Nature, 2019, accessed on 20 July 2020, at https://link.springer.com/
article/10.1186/s41235-019-0181-4.
28 Biometric update, ‘Dark news from dark web: deepfakers are getting their act together’, 2021,
accessed on 16 March 2022, https://www.biometricupdate.com/202105/dark-news-from-dark-web-deepfakers-are-getting-their-act-together.
13

who would be interested in deepfakes as a service. Those who
know how to leverage sophisticated AI can perform the service
for others, enabling threat actors to manipulate a person’s face
and/or voice without understanding the intricacies behind how it
works. Then they can conduct advanced social engineering attacks
on unsuspecting victims, with the aim to make a sizable profit.
Platforms offering these kinds of services have already started
to emerge.29
Law enforcement agencies will be adversely impacted by the rise
of synthetic media and deepfakes. While they may provide some
opportunities to benefit society, this report focuses on the malicious
use of deepfakes. Adverse effects not only include the criminal uses
described in the previous chapter, but also the more general impact
of deepfakes on society. During foresight activities conducted by
Europol, participants discussed how certain technologies could
impact law enforcement. In relation to deepfakes, law enforcement
agencies may even be forced into action, possibly the wrong action,
by misinformation.
Impact on police work
Altered material on social media about events such as
demonstrations may lead to police coming into action
where it is not necessary, or in the wrong place. In police
investigations, law enforcement may chase the wrong suspect
of a crime when a deepfake version of the suspect fleeing
a crime scene goes viral on social media, thereby giving the
suspect the opportunity to get away.
Using deepfakes, people could falsely portray police officers
committing transgressions in order to discredit the police or
even incite violence against officers. In a time where distrust in
authorities is growing, deepfakes and manipulated footage
may be used to negatively affect public opinion. The impact of
such images and footage is not to be underestimated, especially
when this is combined with doxxing (exposing the identity of)
the officers supposedly involved.
Impact on the legal process
In court, audio-visual evidence is usually trusted to be an authentic
representation of events. Whether the file is extracted from the
phone of a suspect, downloaded from social media, or received
from the CCTV system of a shop near the crime scene, the
authenticity of the scene depicted is not usually questioned.
With the rise of deepfakes, it will become increasingly important
29 Europol, ‘Malicious Uses and Abuses of Artificial Intelligence’, 2020, accessed on 10 March
2022, https://www.europol.europa.eu/publications-events/publications/malicious-uses-and-abuses-of-artificial-intelligence.
Deepfake
technology’s
impact on law
enforcement
14FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

to scrutinise such content and verify if it is real or somehow
artificially manipulated or generated.
Cross-checking footage will become even more important. It calls
for a thorough vetting of digital evidence with specific attention to
show it can be trusted to be authentic. A consistent and transparent
chain of custody of digital evidence to prove no one could have
doctored the evidence in the investigation is essential. For instance,
as part of a child custody case, the mother of a child tried to
convince the court that her husband behaved violently.
She manipulated an audio recording of the man to make it look like
he was making threats. Although this was not a real deepfake,
it raises questions and concerns.30 What if the manipulated footage
remained unproven as fake?
With lighter-weight neural network structures and advances in
hardware, training and generating time will be significantly reduced.
In the near future, deepfake software will likely be able to generate
full body deepfakes, real-time impersonations, and the seamless
removal of elements within videos. The most recent algorithms can
deliver increasingly higher levels of realism and run in near real time.
New capacities needed
Claims as to the use of deepfake material will require further law
enforcement assessment, leading to new cases and new types
of work. This will result in an increased workload and a push for
law enforcement officers to develop new skills. Fake evidence has
always existed and law enforcement agencies have procedures
in place to assess the value of evidence. These procedures
are developed for the types of forgeries already known and will
have to be updated continuously with the rise of deepfakes. Law
enforcement agencies will need to not only upskill their workforce
to detect deepfakes, but also invest in their technical capabilities
in order to address the upcoming challenges effectively while
respecting fundamental rights.
Law enforcement agencies must consider this issue from multiple
perspectives, when creating, storing, protecting and analysing
audio-visual material. Specifically, they should:
• make use of tested and proven methods when making audio-visual recordings, e.g. certify a certain set-up for use in court and;
• employ technical and organisational safeguards against
tampering, in order to be able to prove the authenticity of
the footage.
Looking beyond law enforcement, general prevention strategies
may be considered to make it harder to use deepfake technology
30 European Parliamentary Research Service, ‘Tackling deepfakes in European policy’,
2021, accessed 15 March 2022, https://www.europarl.europa.eu/RegData/etudes/
STUD/2021/690039/EPRS_STU(2021)690039_EN.pdf
15

on audio-visual material. For example, technical solutions could
be implemented to make deepfakes easier to spot or to increase
markers of authenticity. The Content Authenticity Initiative31 is an
example of efforts to provide a standard for content authenticity
and provenance.
Participants of the Innovation Lab’s foresight activities anticipated
new forms of crime, together with the resulting challenges in terms
of data collection, criminal attribution and the heightened anonymity
of the perpetrators, such as in creating deepfakes for criminal
purposes. Criminals are likely to adopt new modus operandi that
LEAs will be unable to identify or counter. The failure to legislate
for these technologies will further stymie the investigative abilities
of LEAs.
Mitigating these risks requires greater research and funding. Law
enforcement professionals will need to anticipate possible crime
scenarios such as those discussed in this report, and build out their
investigative abilities accordingly. Furthermore, they should work
with relevant stakeholders to ensure that the appropriate legislation
is in place. Greater awareness building and transparency vis-a-vis
the public is also needed to ensure the roll-out of these technologies
is not hamstrung by concerns over privacy and data protection.
Law enforcement has always had to deal with fake evidence
and therefore is in a good position to adapt to the presence
of deepfakes. In order to handle the material LEAs encounter
appropriately, it is important to account for the possibility of
synthetic content with malicious intent. Here we discuss some of
the ways this synthetic content can be uncovered, and preventative
measures that can be taken against this threat.
Manual detection
It is still possible for the vast majority of deepfake content to be
manually detected by looking for inconsistencies. This is a labour
intensive task, which can only be done for a very limited number
of files, and requires appropriate training to be familiar with all the
relevant signs. Moreover, this process is further complicated by
the human predisposition to believe audio-visual content and work
from a truth default perspective.32 That introduces the possibility of
mistakes, both with selecting the files that need to be inspected as
well as the inspection itself.
31 Content Authenticy Initiative, accessed on 10 March 2022, https://contentauthenticity.org.
32 Levine, T.R., ‘Truth-Default Theory (TDT): A Theory of Human Deception and Deception
Detection’ Journal of Language and Social Psychology, 2014, pp. 378-392., https://www.
researchgate.net/publication/273593306_Truth-Default_Theory_TDT_A_Theory_of_
Human_Deception_and_Deception_Detection.
Deepfake detection
16FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

The models generating deepfakes might produce believable images,
but these may still contain imperfections upon closer examination.
A few examples include:
• blurring around the edges of the face;
• lack of blinking;
• light reflection in the eyes;
• inconsistencies in the hair, vein patterns, scars etc.;
• inconsistencies in the background, in subject as well as focus,
depth etc.33
Automated detection
Ideally, a system would scan any digital content and automatically
report on its authenticity. Such a system will most likely never be
perfect, but with increased sophistication of deepfake technology, a
high degree of certainty from such a system could be worth more
than the manual inspection. There have already been efforts to
create this kind of software from organisations such as Facebook34
and security firm McAfee.35 Detection software will look for signs of
manipulation and help the reviewer decide on the authenticity with
an explainable AI report on these signs.
As deepfake creation tools need training data to know what a real
person looks like, most deepfake detection models are trained using
databases of deepfake images. The learned signs of manipulation
are thus based on data of known deepfakes, making it difficult to
know how successful it will be at detecting deepfakes generated
by unknown or updated models. Moreover, a deepfake GAN can
be updated to account for the signs detected by known detection
models in order to force the results to avoid producing these signs
and henceforth go undetected.
Some examples36 of detection technologies that have been
developed in recent years are:
Biological signals
This approach tries to detect deepfakes based on imperfections in
the natural changes in skin colour that arise from the flow of blood
through the face.37
33  Venema, A. E., & Geradts, Z. J., ‘Digital Forensics Deepfakes and the Legal Process,’ 2020,
TheSciTechLawyer, 16(4), pp. 14-23.
34 Michigan State University, MSU, ‘Facebook develop research model to fight deepfakes’, 2021,
accessed on 10 March 2022, https://msutoday.msu.edu/news/2021/deepfake-detection.
35 McAfee, ‘The Deepfakes Lab: Detecting & Defending Against Deepfakes with Advanced AI’,
2020, accessed on 10 March 2022, https://www.mcafee.com/blogs/enterprise/security-operations/the-deepfakes-lab-detecting-defending-against-deepfakes-with-advanced-ai.
36 AIM, ‘Top AI-Based Tools & Techniques For Deepfake Detection’, 2020, accessed on 24
September 2021, https://analyticsindiamag.com/top-ai-based-tools-techniques-for-deepfake-detection.
37 U. A. Ciftci, I. Demir and L. Yin, “FakeCatcher: Detection of Synthetic Portrait Videos using
Biological Signals,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi:
10.1109/TPAMI.2020.3009287.
17

Phoneme-viseme mismatches
For some words the dynamics of the mouth, viseme, are
inconsistent with the pronunciation of a phoneme. Deepfake models
may not correctly combine viseme and phoneme in these cases.38
Facial movements
This approach uses correlations between facial movements
and head movements to extract a characteristic movement of
an individual to distinguish between real and manipulated or
impersonated content.39
Recurrent Convolutional Models
Videos consist of frames which are really just a set of images.
This approach looks for inconsistencies between these frames
with deep learning models.
However, there are also challenges facing deepfake detection
technology.
f Detection algorithms are trained on specific datasets. A slight
alteration of the method used to generate the deepfake may
therefore prevent detection.
f An update to the discriminative model of a GAN for
specific artefacts detected by these systems will fool
the detection software.
f Videos may be compressed or reduced in size, which causes
problems with the reduction in pixels and artefacts, making it
harder to detect the inconsistencies the system looks for.
f It has been shown that databases may be manipulated to
misclassify images with certain identifiers by adding an identifier
to a small part of the dataset (e.g. applying a trigger to 5% of the
images resulted in the misclassification of fake images with the
trigger as real).40
f Increased image forensics and deepfake detection capabilities
drive the increased quality of deepfake videos. GANs can catch
up relatively easily; by updating the discriminator to evade
the detector, the learning capacity based on feedback loops
of those GANs will work to produce a deepfake that can fool
the detector.41
38 Agarwal, S. et al., ‘Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches’, 2020
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020,
accessed on 10 March 2022, https://www.ohadf.com/papers/AgarwalFaridFriedAgrawala_
CVPRW2020.pdf. 
39  Agarwal, S. et al., ‘Protecting world leaders against deep fakes’, Proceedings of the IEEE/
CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp.
38-45, 2019, accessed on 10 March 2022, http://www.hao-li.com/publications/papers/
cvpr2019workshopsPWLADF.pdf.
40 Cao, X. and Gong, N.Z., ‘Understanding the Security of Deepfake Detection’ ArXiv, 2021,
accessed on 18 October 2021, https://arxiv.org/abs/2107.02045.
41 Wired, ‘Deepfakes Aren’t Very Good. Nor Are the Tools to Detect Them’, 2020, accessed on
15 March 2022, https://www.wired.com/story/deepfakes-not-very-good-nor-tools-detect.
18FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

Preventive measures
Organisations that rely on some kind of authorisation by face
or voice biometrics should assess the authorisation process as
a whole. Increasing the robustness of this process is currently
considered as a better course of action than solely implementing
specific deepfake detection systems. Common checks are:
• using audio-visual authorisation rather than just audio;
• demanding live video connection;
• requiring random complicated acts to be performed live in front
of the camera, e.g. move hands across the face.
In order to address the challenges posed by deepfake technology,
it is important to look into what kind of action other actors, including
the online platforms where most deepfakes can and might be
shared, are addressing this threat. This is also influenced by the
current legislative framework, which can ask for mandatory or
voluntary measures. In this section, this report will show some
examples of key online service providers and companies and their
anti-deepfake measures. This chapter will then examine the EU
regulatory framework in this area.
Technology companies
Early in 2020, Meta (formerly Facebook) announced a new policy
banning deepfakes from their platforms.42 Meta said it would
remove AI-edited content that would likely mislead people, but made
it clear that satire or parodies using the same technology would still
be permissible on the platforms. In order for law enforcement to
assess and address the impact of deepfakes on its work, it needs to
be aware of the policies technology companies have put in place, as
it is likely that potential evidence or malicious content will be shared
via these platforms. How technology companies such as Twitter
and Meta regulate deepfake technology will have an extensive
impact on how people will engage with and react to deepfakes.
Examples of company policies:
f Meta (which owns Facebook and Instagram) aims to remove
deepfakes, or otherwise edited media, where “manipulation
isn’t apparent and could mislead, particularly in the case of
video content.” 43
f TikTok bans “Digital Forgeries (Synthetic Media or Manipulated
Media) that mislead users by distorting the truth of events
42 Becoming Human: Artificial Intelligence Magazine, ‘A Look at Deepfakes in 2020’, 2020,
accessed on 15 March 2022, https://becominghuman.ai/a-look-at-deepfakes-in-2020-
13d3fe2b6ef7.
43 Meta, ‘Manipulated media’, accessed on 10 March 2022, https://transparency.fb.com/en-gb/
policies/community-standards/manipulated-media/.
How are other
actors responding
to deepfakes?
19

and cause significant harm to the subject of the video, other
persons, or society.” 44
f Reddit “does not allow content that impersonates individuals
or entities in a misleading or deceptive manner.” This explicitly
includes deepfakes “presented to mislead, or falsely attributed
to an individual or entity.” 45
f Youtube has an existing ban for manipulated media under
the spam, deceptive practices and scam policies of their
community guidelines.46
Many of the policies use ‘intent’ as their barometer for deciding
whether or not to remove a deepfake. However, defining ‘intent’
might prove challenging and highly subjective, since it is based
on the assessment of individual actors. Nonetheless, it seems
that online platforms could play a pivotal role in helping victims of
deepfake technology to identify the perpetrator, but how this looks
in practice remains to be seen. Moreover, technology providers also
have responsibilities in safeguarding positive and legal use of their
technologies and cooperating with law enforcement.
In addition to the policies, various technology companies are
working on deepfake detection technologies. Developing detection
technologies became a priority during the COVID-19 pandemic, and
has gained new attention during the current conflict between Russia
and Ukraine.
f Meta said it had developed an AI tool that detects deepfakes
by reverse engineering a single AI-generated image to track
its origin.47
f Google has released a large dataset of visual deepfakes that has
been incorporated into the FaceForensics benchmark.48
f Microsoft has launched the Microsoft Video Authenticator,
which can analyse a still photo or video to provide a percentage
chance of whether the media has been artificially manipulated.49
44 TikTok, ‘Community Guidelines’, accessed on 10 March 2022, https://newsroom.tiktok.com/
en-us/combating-misinformation-and-election-interference-on-tiktok.
45 Reddit, ‘Updates to Our Policy Around Impersonation‘, 2020, accessed on 10 March 2022,
https://www.reddit.com/r/redditsecurity/comments/emd7yx/updates_to_our_policy_
around_impersonation.
46 Google Support, ‘Misinformation policies’, accessed on 10 March 2022, https://support.
google.com/youtube/answer/10834785.
47 Politico, ‘POLITICO AI: Decoded: Big Tech on the AI Act — AI inventors — Deepfakes’, 2021,
accessed on 10 March 2022, https://www.politico.eu/newsletter/ai-decoded/politico-ai-decoded-big-tech-on-the-ai-act-ai-inventors-deepfakes.
48 Google AI Blog, ‘Contributing Data to Deepfake Detection Research’, 2019, accessed on 10
March 2022, https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.
html.
49 Microsoft, ‘New Steps to Combat Disinformation’, 2020, accessed on 10 March 2022,
https://blogs.microsoft.com/on-the-issues/2020/09/01/disinformation-deepfakes-newsguard-video-authenticator.
20FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

European Union
Regarding legal trends, participants of the foresight activities
noted that at both the national and regional level, European law is
struggling to keep pace with the evolution of technology and
the changing definitions of crime. Participants flagged the need
to establish new regulatory frameworks. These should be sensitive
to contemporary law enforcement challenges (particularly in
the digital realm), as well as to changing ethical norms. Some
participants anticipated greater regulation of the digital sphere
in the coming decade.
The COVID-19 crisis brought more discussion around regulation of
disinformation and deepfake detection tools, but also an increased
use of video conferencing tools with adjustable backgrounds and
other filters bringing manipulated digital realities into our daily lives.
The European Parliament report, ‘Tackling Deepfakes in European
Policy’, explains this and shows that the regulatory landscape in the
European Union related to deepfakes “comprises a complex web of
constitutional norms, as well as hard and soft regulations on both
the EU and the Member State level”.50
The most relevant regulatory framework for law enforcement in
the area of deepfakes will be the AI regulatory framework – which
is still at proposal level and not applicable yet - proposed by the
European Commission. The framework takes a risk-based approach
to the regulation of AI and its applications. Deepfakes are explicitly
covered by the passage about “AI systems used to generate or
manipulate image, audio or video content”, and have to adhere to
certain minimum requirements. Minimum requirements include
marking content as deepfake to make clear that users are dealing
with manipulated footage.” 51
Deepfake detection software used by law enforcement authorities
falls in the category of ‘high-risk’, as it is considered to pose a threat
to the rights and freedoms of individuals. Detection software used
by law enforcement under the AI regulatory framework would only
be permitted under strict safeguards, such as the employment of
risk-management systems and appropriate data governance and
management practices.52
50 European Parliament Research Service, ‘Tackling deepfakes in European policy’, 2021,
accessed on 10 March 2022, https://www.europarl.europa.eu/RegData/etudes/
STUD/2021/690039/EPRS_STU(2021)690039_EN.pdf.
51  European Parliament Research Service, ‘Tackling deepfakes in European policy’, 2021,
accessed on 10 March 2022, https://www.europarl.europa.eu/RegData/etudes/
STUD/2021/690039/EPRS_STU(2021)690039_EN.pdf.
52  European Parliament Research Service, ‘Tackling deepfakes in European policy’, 2021,
accessed on 10 March 2022, https://www.europarl.europa.eu/RegData/etudes/
STUD/2021/690039/EPRS_STU(2021)690039_EN.pdf.
21

As this report shows, in order to effectively address the threats
posed by deepfake technology, legislation and regulation need to
take into account law enforcement needs. Within the regulatory
framework, law enforcement, online service providers and
other organisations need to develop their policies and invest in
detection as well as prevention technology. Policymakers and law
enforcement agencies need to evaluate their current policies
and practices, and adapt them to be prepared for the new reality
of deepfakes.
The strategic foresight activities conducted by the Europol
Innovation Lab identified a series of challenges that LEAs will have
to contend with in the decade ahead. In particular, they identified
risks associated with digital transformation, the adoption and
deployment of new technologies, the abuse of emerging technology
by criminals, accommodating new ways of working and maintaining
trust in the face of an increase of disinformation.
In the months and years ahead, it is highly likely that threat actors
will make increasing use of deepfake technology to facilitate various
criminal acts and conduct disinformation campaigns to influence or
distort public opinion. Advances in machine learning and artificial
intelligence will continue enhancing the capabilities of the software
used to create deepfakes. According to experts, GANs, availability
of public datasets and increased computing power will be the main
drivers of deepfake development in the future and make them more
difficult to distinguish from authentic content.
The increase in use of deepfakes will require legislation to set
guidelines and enforce compliance. Additionally, social networks
and other online service providers should play a greater role in
identifying and removing deepfake content from their platforms.
As the public becomes more educated on deepfakes, there will be
increasing concern worldwide about their impact on individuals,
communities, and democracies.
In the EU there are various policies and regulatory attempts to
address deepfakes. However, law enforcement’s use of technology
to detect deepfakes is considered as ‘high-risk’, according to some
proposals. Therefore, it will be very important to clarify which
practices should be prohibited under the AI regulatory framework.
In order to address the challenges faced with deepfakes, law
enforcement agencies need to prepare and train for deepfake
detection and ensure e-evidence integrity, developing their
capacities as described in this report. The regulatory framework
should also support law enforcement preparedness efforts.
The Europol Innovation Lab is continuously monitoring the
development of disruptive technologies such as deepfakes.
Conclusion
22FACING REALITY? LAW ENFORCEMENT AND THE CHALLENGE OF DEEPFAKES

About the Europol Innovation Lab
Technology has a major impact on the nature of crime. Criminals quickly integrate
new technologies into their modus operandi, or build brand-new business models
around them. At the same time, emerging technologies create opportunities for
law enforcement to counter these new criminal threats. Thanks to technological
innovation, law enforcement authorities can now access an increased number
of suitable tools to fight crime. When exploring these new tools, respect for
fundamental rights must remain a key consideration.
In October 2019, the Ministers of the Justice and Home Affairs Council called
for the creation of an Innovation Lab within Europol, which would develop a
centralised capability for strategic foresight on disruptive technologies to inform
EU policing strategies.
Strategic foresight and scenario methods offer a way to understand and prepare
for the potential impact of new technologies on law enforcement. The Europol
Innovation Lab’s Observatory function monitors technological developments
that are relevant for law enforcement and reports on the risks, threats and
opportunities of these emerging technologies. To date, the Europol Innovation
Lab has organised three strategic foresight activities with EU Member State law
enforcement agencies and other experts.
www.europol.europa.eu