Image Forgery Detection Based on Semantic Image Understanding

Image forensics has been focusing on low-level visual features, paying little attention to high-level semantic information of the image. In this work, we propose the framework for image forgery detection based on high-level semantics with three components of image understanding module, the normal rule bank (NR) holding semantic rules that comply with our common sense, and the abnormal rule bank (AR) holding semantic rules that don't. Ke et al. [1] also proposed a similar framework, but ours has following advantages. Firstly, image understanding module is integrated by a dense image caption model, with no need for human intervention and more hierarchical features. secondly, our proposed framework can generate thousands of semantic rules automatically for NR. Thirdly, besides NR, we also propose to construct AR. In this way, not only can we frame image forgery detection as anomaly detection with NR, but also as recognition problem with AR. The experimental results demonstrate our framework is effective and performs better.