PIXEL ECHO: MULTISCALE PHASE DECOMPOSITION OF GENERATOR FINGERPRINTS

Generated images carry recurring traces locked to the pixel grid. Where these traces originate decides what an image can be attributed to and which operations erase them. We introduce Pixel Echo, a diagnostic measurement that resolves these traces by spatial phase and repetition period. Pixel Echo pools local pixel responses at matching grid positions and exactly decomposes the resulting periodic structure into count-weighted orthogonal increments at periods of 2, 4, 8, and16 pixels. Their strengths define a scale profile for examining how fingerprints vary across decoders and input routes. Controlled comparisons across two decoder families show that reconstructions and generations sharing a decoder exhibit closely aligned patterns, while pixel-head replay links one decoder’s pattern to phase-specific projection weights. Fixed-latent decoder swaps reveal a perioddependent distinction: decoder-associated patterns concentrate at periods up to the upsampling factor, whereas longer-period structure can pass through from the input. Specifically, six ×8 decoders differ at periods 2–8 but retain a 16-pixel component shared with each other and with real photographs, whereas ×16 decoders exhibit decoder-specific patterns at this period. On held-out splits of our class-to-image and text-to-image benchmarks, linear classifiers frozen beforehand achieve 95.1% four-way and 90.9% seven-way source attribution accuracy, increasing to 98.6% and 94.9% when combined with frequency features, at 0–1.4% per-source false-positive rates on real images. These results support closed-set attribution of decoder and export pipelines rather than unique identification of upstream generators, with attribution requiring preservation of the pixel grid. Code, benchmark manifests, frozen experiment definitions and an interactive demo are available at https://pixel-echo.github.io/.