MIRROR: Manifold Ideal Reference ReconstructOR for Generalizable AI-Generated Image Detection

TL;DR

MIRROR introduces a novel approach to detect AI-generated images by verifying their consistency with real-world image manifolds, outperforming existing methods and approaching human-level detection accuracy.

Contribution

The paper proposes MIRROR, a framework that encodes reality priors with a learnable memory bank to improve generalization in AI-generated image detection.

Findings

Outperforms prior methods on 14 benchmarks with 2.1-8.1% gains.

Achieves 89.6% accuracy on Human-AIGI benchmark, surpassing lay users and experts.

Approaches human perceptual limits with scaled pretrained backbones.

Abstract

High-fidelity generative models have narrowed the perceptual gap between synthetic and real images, posing serious threats to media security. Most existing AI-generated image (AIGI) detectors rely on artifact-based classification and struggle to generalize to evolving generative traces. In contrast, human judgment relies on stable real-world regularities, with deviations from the human cognitive manifold serving as a more generalizable signal of forgery. Motivated by this insight, we reformulate AIGI detection as a Reference-Comparison problem that verifies consistency with the real-image manifold rather than fitting specific forgery cues. We propose MIRROR (Manifold Ideal Reference ReconstructOR), a framework that explicitly encodes reality priors using a learnable discrete memory bank. MIRROR projects an input into a manifold-consistent ideal reference via sparse linear combination, and uses the resulting residuals as robust detection signals. To evaluate whether detectors reach the "superhuman crossover" required to replace human experts, we introduce the Human-AIGI benchmark, featuring a psychophysically curated human-imperceptible subset. Across 14 benchmarks, MIRROR consistently outperforms prior methods, achieving gains of 2.1% on six standard benchmarks and 8.1% on seven in-the-wild benchmarks. On Human-AIGI, MIRROR reaches 89.6% accuracy across 27 generators, surpassing both lay users and visual experts, and further approaching the human perceptual limit as pretrained backbones scale. The code is publicly available at: https://github.com/349793927/MIRROR