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

翻译：高保真生成模型已显著缩小合成图像与真实图像之间的感知差距，对媒体安全构成严重威胁。现有的大多数AI生成图像（AIGI）检测器依赖于基于伪影的分类方法，难以泛化至不断演化的生成痕迹。相比之下，人类判断依赖于稳定的现实世界规律性，偏离人类认知流形的偏差可作为更具泛化性的伪造信号。受此启发，我们将AIGI检测重新定义为参考-比较问题，即验证与真实图像流形的一致性，而非拟合特定的伪造线索。我们提出MIRROR（流形理想参考重构器），该框架通过可学习的离散记忆库显式编码现实先验。MIRROR通过稀疏线性组合将输入投影至流形一致的理想参考，并将生成的残差作为鲁棒的检测信号。为评估检测器是否达到替代人类专家所需的“超人类交叉点”，我们引入了Human-AIGI基准测试集，其中包含通过心理物理学方法筛选的人类不可感知子集。在14个基准测试中，MIRROR始终优于现有方法，在六个标准基准上实现2.1%的性能提升，在七个野外基准上实现8.1%的提升。在Human-AIGI测试中，MIRROR在27种生成器上达到89.6%的准确率，超越普通用户和视觉专家，并随着预训练骨干网络规模的扩大进一步逼近人类感知极限。代码已公开于：https://github.com/349793927/MIRROR