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.
@article{arxiv.2510.05633,
title = {Beyond Spectral Peaks: Interpreting the Cues Behind Synthetic Image Detection},
author = {Sara Mandelli and Diego Vila-Portela and David Vázquez-Padín and Paolo Bestagini and Fernando Pérez-González},
journal= {arXiv preprint arXiv:2510.05633},
year = {2025}
}