Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning

arXiv:2403.08806v1 Announce Type: cross
Abstract: Deepfake technology has raised concerns about the authenticity of digital content, necessitating the development of effective detection methods. However, the widespread availability of deepfakes has given rise to a new challenge in the form of adversarial attacks. Adversaries can manipulate deepfake videos with small, imperceptible perturbations that can deceive the detection models into producing incorrect outputs. To tackle this critical issue, we introduce Adversarial Feature Similarity Learning (AFSL), which integrates three fundamental deep feature learning …

abstract adversarial adversarial attacks arxiv attacks authenticity availability challenge concerns cs.cv cs.lg cs.mm deepfake deepfakes detection detection methods development digital digital content feature form however robust small technology type via videos