Modulo Folding: Rethinking AI-Generated Image Detection Beyond Low-Bit Planes

Reliable detection of AI-generated images is increasingly important for media forensics and trustworthy visual content. Recent studies often use image-based reconstruction errors as an important feature for determining whether an image is AI-generated. However, these approaches typically incur high computational costs and also fail to capture intrinsic noisy features present in the raw images. The recently proposed LOTA method assumes that low-bit planes provide the key discriminative cue and uses bit-plane guided noise generation for detection, which achieves good performance on AI-generated image detection. However, the underlying mechanism of LOTA remains unclear. In this paper, we reveal that LOTA's bit-plane guided noise generation is mathematically equivalent to taking the image modulo eight. This equivalence shows that the essential ingredient is not the bit-plane representation itself but periodic modulo folding. Indeed, we observe that a broad range of modulo periods achieve comparable performance. We provide a theoretical explanation for this phenomenon: modulo folding compresses the large-amplitude semantic component into a small remainder while preserving small-amplitude artifacts, thereby substantially increasing the relative variance and information of artifacts. Based on this finding, we propose Multi-Period Modulo Folding Detector (MP-MFD), which generalizes the single-period modulo eight design of LOTA to multiple periods and injects the resulting folded images into different stages of a backbone network to capture complementary multi-level artifact cues. MP-MFD requires no reconstruction or iterative sampling and treats LOTA as a special case. Extensive experiments show that MP-MFD achieves SOTA results. These results validate the effectiveness of modulo folding.