Beyond Spectral Peaks: Interpreting the Cues Behind Synthetic Image Detection

Over the years, the forensics community has proposed several deep learning-based detectors to mitigate the risks of generative AI. Recently, frequency-domain artifacts (particularly periodic peaks in the magnitude spectrum), have received significant attention, as they have been often considered a strong indicator of synthetic image generation. However, state-of-the-art detectors are typically used as black-boxes, and it still remains unclear whether they truly rely on these peaks. This limits their interpretability and trust. In this work, we conduct a systematic study to address this question. We propose a strategy to remove spectral peaks from images and analyze the impact of this operation on several detectors. In addition, we introduce a simple linear detector that relies exclusively on frequency peaks, providing a fully interpretable baseline free from the confounding influence of deep learning. Our findings reveal that most detectors are not fundamentally dependent on spectral peaks, challenging a widespread assumption in the field and paving the way for more transparent and reliable forensic tools.

Key Contributions

• Proposes a systematic strategy to remove spectral peaks from images and measure their impact on multiple synthetic image detectors
• Introduces a simple, fully interpretable linear detector relying exclusively on frequency-domain peaks as a controlled baseline
• Empirically challenges the widespread assumption that state-of-the-art detectors depend fundamentally on spectral peak artifacts

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