MTFDN: An image copy-move forgery detection method based on multi-task learning

Image copy-move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy-move forgery detection from the perspective of multi-task learning and summarize two characteristics of this problem: (1) Homology and (2) Manipulated traces. Consequently, we propose a multi-task forgery detection network (MTFDN) for image copy-move forgery localization and source/target distinguishment. The network consists of a hard-parameter sharing feature extractor, global forged homology detection (GFHD) and local manipulated trace detection (LMTD) modules. The difference of feature distribution between the GFHD module and the LMTD module is significantly reduced by sharing parameters. Experimental results on several benchmark copy-move forgery datasets demonstrate the effectiveness of our proposed MTFDN.