Deepfake Sentry: Harnessing Ensemble Intelligence for Resilient Detection and Generalisation
This work addresses the problem of robust deepfake detection for security and misinformation prevention, but it is incremental as it builds on existing ensemble and augmentation methods.
The paper tackles the vulnerability of deepfake detection to evolving generation techniques and adversarial attacks by proposing an ensemble autoencoder-based data augmentation approach, which improves generalization and resistance to perturbations, compression, and adversarial attacks across three datasets.
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.