An Interpretable Image Tampering Detection Approach Based on Cooperative Game

In order to reply the potential security issues caused by the tampering of digital images, many image forensics approaches based on deep learning have been proposed in recent years. However, the interpretability of deep learning-based approaches has not been fully considered. In this paper, an interpretable image tampering detection approach is proposed. It consists of suspicious tampered region detection (STRD) module and cooperative game module. The STRD module, inspired by YOLO, combines the shallow-level and deep-level features to discriminate different tampering types of suspicious tampered regions in complex scenes, and also performs well in detecting small tampered regions. The prediction of STRD module could be extended to suspicious box and the payoff of cooperative game module. The cooperative game module utilizes the Shapley interaction index as the strategy to measure the information gain of image pixels. The Shapley interaction index disentangles the multi-order interaction between the pixels of the image, and we discover that image tampering mainly affects low-order interaction of the image. The final detection result is obtained by combining the pixels that contribute greatly to the payoff with the suspicious box. The proposed approach provides a new thought for the interpretability of image forensics and could be broadly applied to other digital image forensic approaches. Extensive experimental results have demonstrated the proposed approach outperforms SOTA approaches, which also has good interpretability and robustness.