Masked Quaternion Phase Modeling for AI-Generated Image Detection

Since Oppenheim and Lim's classic experiments, the phase of the Fourier spectrum is known to carry the structural content of an image. The spectral representations currently used to detect AI-generated images nevertheless rely almost entirely on the amplitude spectrum. We build a detector on the phase itself. We hypothesize that a real image inherits phase consistency from its physical imaging pipeline, across frequency bands and color channels, and that a generated image fails to reproduce this consistency. To capture this consistency, we take the quaternion Fourier transform (QFT) of the RGB image. Unlike three separate transforms, the QFT places the three color channels in one shared phase frame, as a luminance and a chrominance component whose relative phase is well defined. We pretrain a vision transformer on real images only to restore the quaternion phase of masked spectral regions and freeze the model, with its decoder, as the backbone. On this backbone, we build two scores: a phase-deviation score that measures how far an image departs from real-image phase structure without any generated image, and the score of a lightweight attention specialist trained on a single generator. Their decisions are combined by a logical OR at thresholds set on real training images. In the GenImage benchmark, real and generated images differ in compression format and resolution, and these alone predict the label. Under a protocol that canonicalizes both properties for every method, the detector averages 88.2% accuracy over eight generators, 14.9 points above the best of six retrained baselines. QPP transfers zero-shot to the nine generators of Synthbuster and remains stable under JPEG recompression down to a quality factor of 50. Controlled ablations attribute the gain to supervising the luminance–chrominance relative phase, a quantity defined in the shared phase frame that per-channel objectives leave unsupervised.