Adversarial and generative AI-based anti-forensics in audio-visual deepfake detection: A comprehensive review and analysis

초록

As the technology behind deepfakes advances, detecting audio-visual deepfakes becomes more and more crucial, and the rise of traditional and generative AI-based adversarial/anti-forensics attacks and generative AI-based anti-forensics attacks on deepfake detection technologies is a growing concern. Securing applications against adversarial and generative AI-based attacks is critical for accurate and robust deepfake detection tools. Therefore, this paper provides a comprehensive overview of various adversarial and generative AI-based anti-forensic attacks, which represent one of the core elements of trustworthiness alongside transparency, explainability, and fairness, as well as defensive countermeasures for audio-visual deepfake generation and detection. It covers topics such as adversarial attacks on deepfake detection algorithms and defensive methods, including model fusion and decoy-based approaches, to mitigate these threats. Although extensive research has been conducted in recent years on adversarial attacks and defense on deepfake detection, there have been few attempts to compare existing work qualitatively and quantitatively. This paper aims to help identify and address key issues that need to be considered to bring transferable adversarial attacks and their countermeasures particularly through techniques such as generative defense, knowledge distillation, and beyond.

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