MiLD: Mining Informative Low-Bit Cues for Training-Free AI-Generated Image Detection

Low-bit planes have been shown to contain discriminative information for AI-generated image detection. We further observe that the Hamming states induced by low-bit morphological residuals provide useful real/fake cues. However, the Hamming-state occupancy is a static image-level statistic and is sensitive to JPEG compression and Gaussian noise. This motivates us to use Hamming states as spatial cues rather than as the final detection score, and to construct a stable, robust, and efficient training-free detector. We propose MiLD, which selects channel-disagreement locations from low-bit morphological residuals and builds a masked low-bit reversal. MiLD then compares the frozen encoder features of the original and reversed images, using their feature discrepancy as the detection score. Across five benchmarks, MiLD improves the mean AUROC over RIGID by 2.78% while achieving the highest efficiency score. Extensive ablations further demonstrate the stability, transferability, and robustness of MiLD.