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arXiv · 2510.05633

Beyond Spectral Peaks: Interpreting the Cues Behind Synthetic Image Detection

Sara Mandelli·Diego Vila-Portela·David Vázquez-Padín·Paolo Bestagini·Fernando Pérez-González
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Abstract

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.

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Sara Mandelli, Diego Vila-Portela, David Vázquez-Padín, Paolo Bestagini, Fernando Pérez-González. 2025-10-07. Beyond Spectral Peaks: Interpreting the Cues Behind Synthetic Image Detection. https://arxiv.org/abs/2510.05633

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