Micro-Defects Expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts

Overview

MDMF (Micro-Defects expose Macro-Fakes) reframes AI-generated image detection as a local distributional problem rather than an image-level classification one. Instead of compressing each image into a single representation that tends to over-rely on global semantics, MDMF treats every image as a collection of patches, projects each patch into a learnable forensic latent space — the Patch Forensic Signature (PFS) — and measures the distributional discrepancy between the test image and a small reference bank of clean real images via a Maximum Mean Discrepancy (MMD) score.

Key Highlights

• Patch Forensic Signature (PFS) — A learnable forensic reparameterization of frozen DINOv2 patch tokens that suppresses semantic invariances and amplifies generation-induced statistical irregularities.
• MDMF detector — A distribution-aware detection framework that aggregates patch-level evidence into a stable image-level score via MMD between PFS distributions, avoiding the per-patch decision boundary that destabilizes hard-voting baselines.
• Theory-grounded — We prove that patch-wise PFS modeling yields a provably larger MMD signal than global pooling whenever localized forensic cues are present, and we derive a finite-sample separation guarantee with a finite optimal patch count $K^\star$.
• Strong cross-generator generalization — Trained on a single 4-class ProGAN split, MDMF reaches 95.65 average AUROC on the ImageNet benchmark (9 generators spanning diffusion, GAN, and AR families) and remains state-of-the-art on LSUN-Bedroom, GenImage, WildRF, LDMFakeDetect, and an OpenSora video-frame stress test, with markedly gentler degradation under JPEG, blur, and noise post-processing than the strongest training-based baseline.