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Diffusion Artifacts

The subtle, repeatable patterns that diffusion models leave in their output — a primary target for AI image detectors.

Diffusion artifacts are the systematic traces a diffusion model leaves behind because of how it generates an image: starting from noise and iteratively denoising toward a result. That process imprints statistical regularities — in the frequency domain, in local texture, and at object boundaries — that real cameras don't produce.

The most detectable artifacts live in the high-frequency band. Iterative denoising tends to leave periodic structure that shows up clearly under a Fourier transform, which is why frequency-domain analysis is the workhorse of modern image detectors. Other artifacts include over-smooth texture in flat regions, subtly inconsistent lighting, and small errors in fine repeated detail (text, jewelry, distant faces).

Different model families leave distinguishable artifacts. Stable Diffusion, Midjourney, DALL-E, and FLUX each have characteristic signatures, which is how some forensic tools attempt model attribution — guessing which generator made an image.

SynthGuard's humanizer targets these artifacts directly. FFT disruption adds calibrated noise in the bands where diffusion regularities concentrate, texture perturbation breaks the over-smoothness, and PRNU injection adds the sensor noise the artifacts lack. The goal is to replace a synthetic statistical signature with one that matches a real capture.