RAID: Towards Robust AI-Generated Image Detection with Bit-Reversed Images

Researchers from Southeast University propose RAID, a new AI-generated image detection method that uses bit-reversed images and a gradient-based patch selection with a convolutional classifier, achieving state-of-the-art performance on over 40 benchmarks and running nearly 100 times faster than existing methods. The approach, detailed in arXiv:2607.28974v1, introduces two new challenging datasets and includes theoretical analysis to validate its effectiveness.

arXiv:2607.28974v1 Announce Type: new Abstract: The rapid advancement of image generation models has made it increasingly difficult for people to distinguish AI-generated images from real ones. To prevent the potential risks associated with the misuse of fake images, AI-generated image detection has gained significant attention. Existing methods neglect the inherent differences between real and fake images, thus lacking robustness and generalization ability. In this work, we innovatively investigate AI-generated image detection using bit-planes, and introduce the bit-reversed image. We propose a simple yet effective pipeline consisting of construction of bit-reversed images, gradient-based patch selection and a convolutional classifier. Besides, we provide a theoretical analysis from the mathematical perspective to demonstrate the validity of our approach. We also introduce two challenging datasets for AI-generated image detection. Extensive experiments verify the effectiveness of our approach across different settings, including cross-generator generalization, cross-dataset generalization and zero-shot performance. Without bells and whistles, our approach outperforms existing methods on over 40 benchmarks, and is nearly 100 times faster than counterparts. The code is at https://github.com/renxi-seu/RAID.