Generalized Fake Image Detection Method Based on Gated Hierarchical Multi-Task Learning

Recently, the abuse of image generation techniques based on artificial intelligence has posed a great threat to the integrity of digital images. However, existing detection methods are hard to provide generalized detection capability of fake images generated by unseen models. To address this issue, we propose a generalized fake image detection framework based on gated hierarchical multi-task learning, which is supervised by well-designed forensics sub-tasks. Firstly, a global artifact learning task is constructed as binary classification with region masking augmentation. Besides, a block-wise spatial correlation learning task is designed by solving jigsaw puzzle cooperated with color jitter operations, which aims to explore common artifacts of various generators. Finally, a hierarchical multi-task learning paradigm is developed with multi-gate structures, which can adjust the importance of different forensics clues and jointly enhance detection performance. Extensive experiments have been conducted to evaluate the superiority of the proposed method on the open-set scenario with unseen generators.