Efficient Zero-Shot AI-Generated Image Detection

• URL: http://arxiv.org/abs/2603.21619v1
• Date: Mon, 23 Mar 2026 06:33:20 GMT
• Title: Efficient Zero-Shot AI-Generated Image Detection
• Abstract summary: We propose a training-free AI-generated image detection method that measures representation sensitivity to structured frequencys.<n>The proposed method is computationally lightweight, as perturbation generation requires only a single Fourier transform for an input image.<n>Extensive experiments on challenging benchmarks demonstrate the efficacy of our method over state-of-the-art (SoTA)
• Score: 2.915868985330569
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid progress of text-to-image models has made AI-generated images increasingly realistic, posing significant challenges for accurate detection of generated content. While training-based detectors often suffer from limited generalization to unseen images, training-free approaches offer better robustness, yet struggle to capture subtle discrepancies between real and synthetic images. In this work, we propose a training-free AI-generated image detection method that measures representation sensitivity to structured frequency perturbations, enabling detection of minute manipulations. The proposed method is computationally lightweight, as perturbation generation requires only a single Fourier transform for an input image. As a result, it achieves one to two orders of magnitude faster inference than most training-free detectors.Extensive experiments on challenging benchmarks demonstrate the efficacy of our method over state-of-the-art (SoTA). In particular, on OpenFake benchmark, our method improves AUC by nearly $10\%$ compared to SoTA, while maintaining substantially lower computational cost.

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