Multi-Strategy Adversarial Learning for Robust Face Forgery Detection Under Heterogeneous and Composite Attacks

Face forgery detection has recently progressed to address the threat from image synthesis technology, although robust face forgery detection under heterogeneous attacks remains challenging. When forgers leverage image post-processing techniques to manipulate forged photos, recent detection methods exhibit significant performance degradation. In this work, we propose a novel multi-strategy adversarial learning (MAL) method to extract salient features in order to achieve more reliable forgery detection under attacks. In particular, our MAL framework creates a large number of positive and negative sample pairs by designing a composite attack generation module with supervised contrastive training to ensure the attack robustness. In addition, we exploit two intuitive strategies, hard sample selection and region consistency, to enhance the contrastive losses for further strengthened feature reliability. Extensive experimental results demonstrate our proposed method to outperform recent state-of-the-art face forgery detection methods in terms of overall accuracy under various single and composite attacks.