Leveraging Natural Frequency Deviation for Diffusion-Generated Image Detection

Daichi Zhang, Tong Zhang, Shiming Ge, Sabine Susstrunk

Keywords: frequency domain, filter banks, diffusion-generated image detection

Abstract:

Diffusion models have achieved remarkable success in image synthesis, but the generated high-quality images raise concerns about potential malicious use. Existing detectors often struggle to capture distinctive features across different training models, limiting their generalization to unseen diffusion models with varying schedulers and hyperparameters. To address this issue, we observe that diffusion-generated images exhibit progressively larger differences from real images across low- to high-frequency bands. Based on this insight, we propose a novel image representation called \textbf{N}atural \textbf{F}r\textbf{e}quency \textbf{De}viation~(\textbf{DEFEND}). DEFEND applies a weighted filter to the Fourier spectrum, suppressing less discriminative bands while enhancing more informative ones. This approach, grounded in a comprehensive analysis of frequency-based differences between real and diffusion-generated images, enables robust detection of images from unseen diffusion models and provides resilience to various perturbations. Extensive experiments on diffusion-generated image datasets show that our method outperforms state-of-the-art detectors with superior generalization and robustness.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 3874