GAN Fingerprint
A pattern unique to a specific generative model that detectors use to identify the source generator.
Generative Adversarial Networks (GANs) leave architectural fingerprints in their outputs — periodicities and textural patterns specific to the model's upsampling layers. Diffusion models leave similar but distinct fingerprints from their iterative denoising structure.
Forensic detectors can sometimes identify the source model from the fingerprint alone — Stable Diffusion 1.5 vs SDXL vs Midjourney v6 vs FLUX. This is useful for journalism (proving an image came from a specific generator) and dangerous for creators (a single fingerprint match is enough for a flag).
SynthGuard's texture perturbation layer attacks the spatial-domain fingerprints. Combined with FFT disruption (frequency-domain) the model-of-origin classification collapses.
This is one of the more sophisticated detection axes — most consumer detectors don't run model-attribution, but academic and forensic tools do.
Tools that address GAN Fingerprint
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