Background-Noise-Driven Detection of Diffusion-Generated Images

Diffusion-based image generation has proliferated, making robust detection of synthetic imagery critical. We propose a background-noise-driven detector motivated by the observation that real camera images preserve physical noise traces, whereas model-generated images tend to exhibit algorithm-induced residual statistics. Starting from sRGB, we apply a deterministic inverse ISP to obtain an approximate Bayer RAW representation, train a lightweight RAW-domain denoiser, and extract a noise residual by subtracting the denoised reconstruction from the inverse-ISP signal. A detector trained only on ADM residuals generalizes in a zero-shot manner to a wide range of unseen diffusion generators. By emphasizing noise-domain cues rather than semantic content, the proposed method offers a simple and practical approach to diffusion-generated image detection.