Towards Universal AI-Generated Image Detection by Variational Information Bottleneck Network

The rapid advancement of generative models has significantly improved the quality of generated images. Meanwhile, it challenges information authenticity and credibility. Current generated image detection methods based on large-scale pre-trained multimodal models have achieved impressive results. Although these models provide abundant features, the authentication task-related features are often submerged. Consequently, those authentication task-irrelated features cause models to learn superficial biases, thereby harming their generalization performance across different model genera (e.g., GANs and Diffusion Models). To this end, we proposed VIB-Net, which uses Variational Information Bottlenecks to enforce authentication task-related feature learning. We tested and analyzed the proposed method and existing methods on samples generated by 17 different generative models. Compared to SOTA methods, VIB-Net achieved a 4.62% improvement in mAP and a 9.33% increase in accuracy. Notably, in generalization tests on unseen generative models from different series, VIB-Net improved mAP by 12.48% and accuracy by 23.59% over SOTA methods.