Distinguishing between real and AI-generated images, commonly referred to as ímage detection', presents a timely and significant challenge. Despite extensive research in the (semi-)supervised regime, zero-shot and few-shot solutions have only recently emerged as promising alternatives. Their main advantage is in alleviating the ongoing data maintenance, which quickly becomes outdated due to advances in generative technologies. We identify two main gaps: (1) a lack of theoretical grounding for the methods, and (2) significant room for performance improvements in zero-shot and few-shot regimes. Our approach is founded on understanding and quantifying the biases inherent in generated content, where we use these quantities as criteria for characterizing generated images. Specifically, we explore the biases of the implicit probability manifold, captured by a pre-trained diffusion model. Through score-function analysis, we approximate the curvature, gradient, and bias towards points on the probability manifold, establishing criteria for detection in the zero-shot regime. We further extend our contribution to the few-shot setting by employing a mixture-of-experts methodology. Empirical results across 20 generative models demonstrate that our method outperforms current approaches in both zero-shot and few-shot settings. This work advances the theoretical understanding and practical usage of generated content biases through the lens of manifold analysis.
View on arXiv@article{brokman2025_2504.15470,
title={ Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images },
author={ Jonathan Brokman and Amit Giloni and Omer Hofman and Roman Vainshtein and Hisashi Kojima and Guy Gilboa },
journal={arXiv preprint arXiv:2504.15470},
year={ 2025 }
}