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

2026-07-30

AI Summary

This work addresses the limited robustness and generalization of existing AI-generated image detection methods, which often overlook intrinsic discrepancies between real and synthetic images. The study pioneers a bit-plane perspective to analyze generative artifacts, introducing the concept of bit-flipped images and establishing its theoretical foundation. Building upon this insight, the authors propose an efficient detection framework comprising bit-flip image generation, gradient-guided local region selection, and a lightweight convolutional classifier. Evaluated across more than 40 benchmark settings, the method substantially outperforms current state-of-the-art approaches, demonstrating exceptional cross-generator, cross-dataset, and zero-shot generalization capabilities, while achieving nearly a 100-fold speedup in inference time.

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.

Problem

• AI-generated image detection
• robustness
• generalization
• bit-reversed images
• fake image detection

Innovation

• bit-reversed image
• AI-generated image detection
• bit-plane analysis
• cross-generator generalization
• robust detection