DEEPFAKE SENTRY: HARNESSING ENSEMBLE INTELLIGENCE FOR RESILIENT DETECTION AND GENERALISATION

Recent advancements in Generative Adversarial Networks (GANs) have enabled photorealistic image generation with high quality. However, the malicious use of such generated media has raised concerns regarding visual misinformation. Although deepfake detection research has demonstrated high accuracy, i...

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Bibliographic Details
Published inScientific Bulletin. Series C, Electrical Engineering and Computer Science no. 4; p. 193
Main Authors Stefan, Liviu-Daniel, Stanciu, Dan-Cristian, Dogariu, Mihai, Constantin, Mihai Gabriel, Jitaru, Andrei Cosmin, Ionescu, Bogdan
Format Journal Article
LanguageEnglish
Published Bucharest University Polytechnica of Bucharest 01.01.2023
Subjects
Affine transformations
Algorithms
Blurring
Data augmentation
Deception
Generative adversarial networks
Image compression
Image processing
Image quality
Machine learning
Resilience
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Summary:Recent advancements in Generative Adversarial Networks (GANs) have enabled photorealistic image generation with high quality. However, the malicious use of such generated media has raised concerns regarding visual misinformation. Although deepfake detection research has demonstrated high accuracy, it is vulnerable to advances in generation techniques and adversarial iterations on detection countermeasures. To address this, we propose a proactive and sustainable deepfake training augmentation solution that introduces artificial fingerprints into models. We achieve this by employing an ensemble learning approach that incorporates a pool of autoencoders that mimic the effect of the artefacts introduced by the deepfake generator models. Experiments on three datasets reveal that our proposed ensemble autoencoder-based data augmentation learning approach offers improvements in terms of generalisation, resistance against basic data perturbations such as noise, blurring, sharpness enhancement, and affine transforms, resilience to commonly used lossy compression algorithms such as JPEG, and enhanced resistance against adversarial attacks.
ISSN:2286-3540