Beyond Spectral Peaks: Interpreting the Cues Behind Synthetic Image Detection

2025-10-07

AI Summary

Existing deep learning-based synthetic image detectors are commonly assumed to rely on periodic spectral peaks in the frequency domain—a purported key indicator of synthetic origin—yet this assumption lacks empirical validation, undermining model interpretability and trustworthiness. To address this, we systematically assess the true dependence of mainstream detectors on such spectral peaks via spectral analysis, targeted peak masking experiments, and deep model behavior attribution. Our findings reveal that most detectors do not fundamentally rely on these features. Leveraging this insight, we propose the first linear detector operating solely on frequency-domain peaks: fully interpretable, training-free, and competitive with several state-of-the-art models. This work challenges a long-standing domain assumption, establishes a transparent and reliable forensic baseline, and introduces a novel paradigm for explainable AI-driven image authenticity verification.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Investigates whether synthetic image detectors truly rely on spectral peaks
• Proposes method to remove spectral peaks and analyze detector performance
• Reveals most detectors do not fundamentally depend on frequency artifacts

Innovation

• Removing spectral peaks to test detector reliance
• Introducing linear detector using only frequency peaks
• Challenging assumption of spectral peak dependency