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

High-fidelity generative models have narrowed the perceptual gap between synthetic and real images, putting media security under increasing pressure. Most AI-generated image (AIGI) detectors classify by artifact and fail to generalise to new generators. We argue that anchoring detection on the stationary real-image distribution gives a more generalisable signal than chasing the non-stationary forgery distribution, and cast AIGI detection as a Reference-Comparison problem that checks consistency against a learned real-domain feature subspace rather than fitting specific forgery cues. We propose **MIRROR** (**M**anifold **I**deal **R**eference **R**econstruct**OR**), which encodes real-image priors in a learnable discrete memory bank, projects an input onto these prototypes via sparse linear combination to form a manifold-consistent ideal reference, and uses the Comparison Residual and Reconstruction Perplexity as detection signals. To mark the Superhuman Crossover where detectors surpass human experts, we build the Human-AIGI benchmark with a psychophysically curated human-imperceptible subset. Across 14 benchmarks, MIRROR improves over prior methods by 2.1% on six standard benchmarks and 8.1% on seven in-the-wild benchmarks. On Human-AIGI it reaches 89.6% fake accuracy over 27 generators, sitting between the computer-vision-expert and AIGI-detection-expert reference levels, and the gap to the AIGI-detection-expert level closes further as the pretrained backbone scales.