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

作者：Haifeng Zhang, Qun He, Xiuli Bi, Weisheng Li, Бо Лю, Bin Xiao · 年份：2025 · DOI：10.1109/cvpr52734.2025.02219 · 被引用次数：7 · 研究领域：Brain Tumor Detection and Classification、AI in cancer detection、Advanced Neural Network Applications

The rapid advancement of generative models has significantly improved the quality of generated images. Mean-while, 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 5.55% 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. The code is available at https://github.com/oceanzhf/VIBAIGCDetect.