Thales’s Friendly Hackers Unit Invents Metamodel to Detect AI-generated Deepfake Images
November 20 2024 - 3:00AM
Business Wire
- As part of the challenge organised by France's Defence
Innovation Agency (AID) to detect images created by today’s AI
platforms, the teams at cortAIx, Thales’s AI accelerator, have
developed a metamodel capable of detecting AI-generated
deepfakes.
- The Thales metamodel is built on an aggregation of models, each
of which assigns an authenticity score to an image to determine
whether it is real or fake.
- Artificially generated AI image, video and audio content is
increasingly being used for the purposes of disinformation,
manipulation and identity fraud.
Artificial intelligence is the central theme of this year’s
European Cyber Week from 19-21 November in Rennes, Brittany. In a
challenge organised to coincide with the event by France's Defence
Innovation Agency (AID), Thales teams have successfully developed a
metamodel for detecting AI-generated images. As the use of AI
technologies gains traction, and at a time when disinformation is
becoming increasingly prevalent in the media and impacting every
sector of the economy, the deepfake detection metamodel offers a
way to combat image manipulation in a wide range of use cases, such
as the fight against identity fraud.
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AI-generated images are created using AI platforms such as
Midjourney, Dall-E and Firefly. Some studies have predicted that
within a few years the use of deepfakes for identity theft and
fraud could cause huge financial losses. Gartner has estimated that
around 20% of cyberattacks in 2023 likely included deepfake content
as part of disinformation and manipulation campaigns. Their report1
highlights the growing use of deepfakes in financial fraud and
advanced phishing attacks.
“Thales’s deepfake detection metamodel addresses the problem of
identity fraud and morphing techniques,”2 said Christophe
Meyer, Senior Expert in AI and CTO of cortAIx, Thales’s AI
accelerator. “Aggregating multiple methods using neural
networks, noise detection and spatial frequency analysis helps us
better protect the growing number of solutions requiring biometric
identity checks. This is a remarkable technological advance and a
testament to the expertise of Thales’s AI researchers.”
The Thales metamodel uses machine learning techniques, decision
trees and evaluations of the strengths and weaknesses of each model
to analyse the authenticity of an image. It combines various
models, including:
- The CLIP method (Contrastive Language-Image Pre-training)
involves connecting image and text by learning common
representations. To detect deepfakes, the CLIP method analyses
images and compares them with their textual descriptions to
identify inconsistencies and visual artefacts.
- The DNF (Diffusion Noise Feature) method uses current
image-generation architectures (called diffusion models) to detect
deepfakes. Diffusion models are based on an estimate of the amount
of noise to be added to an image to cause a “hallucination”, which
creates content out of nothing, and this estimate can be used in
turn to detect whether an image has been generated by AI.
- The DCT (Discrete Cosine Transform) method of deepfake
detection analyses the spatial frequencies of an image to spot
hidden artefacts. By transforming an image from the spatial domain
(pixels) to the frequency domain, DCT can detect subtle anomalies
in the image structure, which occur when deepfakes are generated
and are often invisible to the naked eye.
The Thales team behind the invention is part of cortAIx, the
Group’s AI accelerator, which has over 600 AI researchers and
engineers, 150 of whom are based at the Saclay research and
technology cluster south of Paris and work on mission-critical
systems. The Friendly Hackers team has developed a toolbox called
BattleBox to help assess the robustness of AI-enabled systems
against attacks designed to exploit the intrinsic vulnerabilities
of different AI models (including Large Language Models), such as
adversarial attacks and attempts to extract sensitive information.
To counter these attacks, the team develops advanced
countermeasures such as unlearning, federated learning, model
watermarking and model hardening.
In 2023, Thales demonstrated its expertise during the CAID
challenge (Conference on Artificial Intelligence for Defence)
organised by the French defence procurement agency (DGA), which
involved finding AI training data even after it had been deleted
from the system to protect confidentiality.
About Thales
Thales (Euronext Paris: HO) is a global leader in advanced
technologies specialising in three business domains: Defence &
Security, Aeronautics & Space and Cybersecurity & Digital
Identity.
The Group develops products and solutions that help make the
world safer, greener and more inclusive.
Thales invests close to €4 billion a year in Research &
Development, particularly in key innovation areas such as IA,
cybersecurity, quantum technologies, cloud technologies and 6G.
Thales has 81,000 employees in 68 countries. In 2023, the Group
generated sales of €18.4 billion.
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Developing AI systems we can all trust | Thales Group
1 2023 Gartner Report on Emerging Cybersecurity Risks.
2 Morphing involves gradually changing one face into another in
successive stages by modifying visual features to create a
realistic image combining elements of both faces. The final result
looks like a mix of the two original appearances.
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Marion Bonnet Thales PR Manager Marion.bonnet@thalesgroup.com
+33660384892
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