Image forgery localization with sparse reward compensation using curiosity-driven deep reinforcement learning

Advanced editing and deepfakes make image tampering harder to detect, threatening image security, credibility, and personal privacy. To address this challenging issue, we propose a novel end-to-end image forgery localization method, based on the curiosity-driven deep reinforcement learning method with intrinsic reward. The proposed method provides reliable localization results for forged regions in images of various types of forgery. This study designs a new Focal-based reward function that is suitable for scenarios with highly imbalanced numbers of forged and real pixels. Furthermore, considering the issue of sparse rewards caused by sparse forgery regions in real-world forgery scenarios, we introduce a surprise-based intrinsic reward generation module, which guides the agent to explore and learn the optimal strategy. Extensive experiments conducted on multiple benchmark datasets show that the proposed method outperforms other methods in pixel-level forgery localization. Additionally, the proposed method demonstrates stable robustness to image degradation caused by different post-processing attacks.