Deepfake en de authenticiteit
van videomateriaal
VOGIN ip-lezing
Prof. dr. ing. Zeno Geradts
zelftestenbestellen.nl
zelftestenbestellen.nl

› - Introduction
 NFI expertise
 AI4Forensics ICAI lab
 Deepfakes
› Making deepfakes
› Detecting deepfakes
› Challenges : realtime
deepfakes
› Take home message
Outline
2

3
Beeldonderzoek en Biometrie in het kort
Herstel gewiste beelden:
• Herstel de gewiste beelden op de
harde schijf van de DVR
• Herstel de gewiste beelden op de
smartphone
Authenticiteitsonderzoek:
• Is het voertuig in de video geplakt?
• Is de afgebeelde gebeurtenis een
authentieke weergave van de
werkelijkheid?

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Beeldonderzoek en Biometrie in het kort
Beeldverbetering:
• Tweeluik
• Maak details van persoon beter
zichtbaar
Bronbepaling foto/video:
• Is de foto gemaakt met de
inbeslaggenomen camera?
• Welke foto’s in de verzameling zijn
met dezelfde camera gemaakt?

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Beeldonderzoek en Biometrie in het kort
Fotogrammetrie:
• Hoe hard reed de auto?
• Hoe lang is de persoon?
Vergelijking gezichtsbeelden:
• Is de persoon in beeld de
verdachte?
• Is de persoon in beeld A dezelfde
als de persoon in beeld B?

Nieuws
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On line deepfake maker
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Faceswap booth
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Lip syncing app
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Everybody can deepfake Deepfacelab
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Make persons that do not exist
thispersondoesnotexist.com
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Make cats that do not exist thiscatdoesnotexist.com
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Ambitie
Creatie van een Forensisch AI lab binnen ICAI
met de Universiteit van Amsterdam en het Nederlands Forensisch Instituut
in samenwerking met verschillende stakeholders
zoals de Nationale Politie, het Openbaar Ministerie, Rechters, en de Advocatuu
om onderzoek te verrichten naar nieuwe AI technieken voor
het forensisch veld

Kansen invulling onderzoekers
• Directe financiering
• PhD Automotive met Politie
• PhDs met Openbaar Ministerie / Zittende Magistratuur / FIOD / PIDS / BZK
• Kansen voor additionale Nationale / EU subsidies
• PhDs in andere EU projecten
• NWO Data and Intelligence
• AI Coalitie
• Taal NAIN
• Potentiele additionale samenwerking
• Forensische radiologie en AI
• Interpretatie van Fysische / Chemische beelden met AI
• NTNU Noorwegen

Basic principles

Machine learning vs deep learning
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Recap: Autoencoder

Basic approach

Video forgeries
- interframe forgery
- intraframe forgery
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It was real
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https://www.youtube.com/watch?v=dn
ZNrp0-mF8

It was a deepfake
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Some issues
- the face is vague, especially at the edges
- look to teeth, mouth and hair they are not detailed
- audio is not synchronized with video
- the body language is different from the way one
should expect to speak
- the light source is different
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Real time frame forgery
With webcam a different background
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Manipulations of faces
- face swap
- face attributes
- face expressions
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lip sync deepfake https://news.berkeley.edu/2019/06/18/researchers-use-facial-quirks-to-unmask-
deepfakes/
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Puppet master
https://spectrum.ieee.org/tech-talk/computing/software/the-worlds-first-audit-of-deepfake-videos-and-tools-on-the-open-web
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Audio deepfake look at github
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Practical issues
Access to the
image;
The skill level
of the artist
necessary to
perform the
manipulation;
The time
necessary to
create the
manipulation;
The
availability of
software and
hardware
necessary to
perform the
manipulation;
The level of
fine detail in the
image; and
The
complexity of
the image
content, such as
physical
interaction of
people with one
another and the
environment.
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Best practices
• Skin tones & textures
• Skeletal structure
• Flesh & muscle movement
• Body-to-object contact
• Skin-to-skin contact
• Skin creases
• Hair
• Ears
• Eyes
• Reaction of subjects/objects to gravity and physics.
• Continuity Issues
•

Video detection of forgeries
• Same as images, also to metadata
• Look to changes of scenes, detect differences
• Time of recording ENF

Other methods in research

Biological signals heartbeat
Heart Beat
"Cardiocam: Technology for Non-Contact Multi-Parameter Physiologic
Measurements". by Ming-Zher Optics Express, Vol. 18, Issue 10,
pp. 10762-10774 (2010) doi:10.1364/OE.18.010762

• Splicing
• Copy/move forgery (C/M-F)
• Contrast Enhancement (CE)
• Resampling (RSa)
• Articial Blur (AB)
Manipulation types
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36

?
PRNU - Camera identification research
disputed
camera Reference cameras
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
disputed
photo
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
PRN
U
0.113 0.093 0.149 0.002 0.003 0.001
0.003 0.002 0.003 0.110 0.104 0.002
0.004 0.001 0.003 0.002 0.001 0.003
0.004 0.002 0.001 0.002 0.001 0.177
0.118
Disputed
Disputed
Ref cam 1
Ref cam 2
Ref cam 10
Ref cam 1 Ref cam 10

How are Deepfakes made?
● Thousands of images of actor A and of
actor B are needed for good results.
● Two autoencoders (A and B) are
trained on these images.
● Autoencoder AB puts face of A on body
of B

Forensic Relevance
● Authenticate video evidence
● Technology widely accessible on the internet
● Easy to use for everyone through GUIs
● Authentication of videos is also important for journalism, social media, etc.

Photo Response Non Uniformity (PRNU)
analysis: Method
● ‘The fingerprint of the digital camera’
● Manipulation can alter PRNU pattern
● Deepfake PRNU pattern less consistent
throughout video compared to Authentic?
● Second order and Wavelet method
○ Second wave = faster
○ Wavelet = more reliable
● Cropped and uncropped
○ Increase % variability of PRNU

PRNU analysis:
Method

PRNU analysis: Conclusions
● Second order > Wavelet method
○ Takes less time
○ Stronger correlation
○ More reliable correlation
● Second order cropped > Second order uncropped
○ Stronger correlation

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Deepfake
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real
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Detection methods

Image based
Extract and annotate each frame individually, then use
a feedforward CNN to classify each frame.
Training
calculate/backpropagate each frame-wise loss
Inference
predict each frame, then aggregate over all frames
to generate video-level prediction
> detect manipulations by consulting spatial features

Video based
Extract and annotate frame windows, then use
spatio-temporal model to predict frame windows.
Training
calculate/backpropagate loss associated with each
frame-window
Inference
predict each frame window, then aggregate over all windows
to generate video-level prediction
> detect manipulations by consulting spatio-temporal features

Why use video-based modeling?
Image based methods work well on in-dataset
evaluations
But: cross-manipulation accuracy is low!
A good detection method should be able to consider
features
that underlie all manipulation methods.
Rossler et al. (2019)
Li et al. (2019)

Arms race
Forensic research on deepfakes has a major
disadvantage:
Deep learning is data hungry
> but we cannot wait for availability of training data for
each manipulation method
Need to find underlying weakness shared
by all manipulations:
> until now: all deepfake generators are image-based!
> frame-to-frame consistencies are not enforced

Data

Celeb-DF
- 5.639 high resolution deepfakes
- generated by a single
manipulation method
- use kalman filter to increase
between-frame correlations of
facial landmarks
- currently the most challenging
set of Deepfakes available
Li et al. (2019)

DFDC - Deepfake Detection Challenge
- 470GB of Deepfake material
- multiple manipulation methods
- diverse in backgrounds, lighting, ethnicity, gender
and number of actors (with a subset or all actors
manipulated)
- unfortunately, most clips far from real-world
quality
Dolhansky et al. (2019)

Real-world data
- collected from the web
- multiple, unknown manipulation methods
- subject to pre- and postprocessing
- handpicked, highest quality Deepfakes available

Facebook 100.000 samples deepfakes / real
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Models

EfficientNet
Tan & Le (2019)
• high
performance on
ImageNet & co
• computationally
manageable
• available pre-
trained on
ImageNet
• most frequently
used model in
high ranking
teams DFDC

Watch out for database that is used
https://openaccess.thecvf.com/content/CVPR2021W/WMF/papers/Neekhara_Adversarial_Threats_to_DeepFake_Detection_A_Practical_Perspective_CVPRW_2021_paper.pdf
60

Convolutional Recurrent Neural Networks
Sabir et al (2019)

Making your own deepfakes
Try deepfacelab also https://github.com/iperov/DeepFaceLab for Windows

Zelf testen
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Duckduckgoose
• Deedetector
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Samenvatting
• ICAI4Forensics lab samenwerking UvA / NFI in opbouw open voor meer
PhDs ook op andere AI onderwerpen
• Deepfakes worden steeds moeilijker te detecteren
• Het maken wordt steeds makkelijker
• Worden in de praktijk ook gebruikt fakenews, maar ook authentieke
videos zijn moeilijk te detecteren of ze echt zijn
• Fake personen eenvoudig te maken op sociale media
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Questions
z.geradts@nfi.nl