![]() ![]() It could sometimes achieve comparable, or better, performance. Our proposed method outperformed existing self-supervised methods in all experiments. ![]() ![]() Experiments and comparisons showed the effectiveness of our method for solving the CG forensics problem under different evaluation scenarios. Differing from existing self-supervised methods, based on pretext tasks targeted at image understanding, or based on contrastive learning, we propose carrying out self-supervised training on a forensics-oriented pretext task of classifying authentic images and their modified versions after applying various manipulations. The idea is to make use of a large number of readily available unlabeled data, along with a self-supervised training procedure on a well-designed pretext task for which labels can be generated in an automatic and convenient way without human manual labeling effort. To our knowledge, we study, for the first time in the literature, the utility of the self-supervised learning mechanism for the forensic classification of CG images and NIs. Previous research works mainly focused on the conventional supervised learning framework, which usually requires a good quantity of labeled data for training. With the increasing visual realism of computer-graphics (CG) images generated by advanced rendering engines, the distinction between CG images and natural images (NIs) has become an important research problem in the image forensics community. ![]()
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