  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.
