With the rapid advancement of vision generation models, the potential security risks stemming from synthetic visual content have garnered increasing attention, posing significant challenges for AI-generated image detection. Existing methods suffer from inadequate generalization capabilities, resulting in unsatisfactory performance on emerging generative models. To address this issue, this paper presents a novel framework that leverages noise-based model-specific imprint for the detection task. Specifically, we propose a novel noise-based imprint simulator to capture intrinsic patterns imprinted in images generated by different models. By aggregating imprints from various generative models, imprints of future models can be extrapolated to expand training data, thereby enhancing generalization and robustness. Furthermore, we design a new pipeline that pioneers the use of noise patterns, derived from a noise-based imprint extractor, alongside other visual features for AI-generated image detection, resulting in a significant improvement in performance. Our approach achieves state-of-the-art performance across three public benchmarks including GenImage, Synthbuster and Chameleon.

翻译：随着视觉生成模型的快速发展，合成视觉内容带来的潜在安全风险日益受到关注，对AI生成图像检测提出了重大挑战。现有方法存在泛化能力不足的问题，导致在新兴生成模型上的性能表现不佳。为解决这一问题，本文提出了一种新颖的框架，利用基于噪声的模型特定印记进行检测任务。具体而言，我们设计了一种新型的基于噪声的印记模拟器，用于捕捉不同模型生成图像中固有的印记模式。通过聚合来自各种生成模型的印记，可以外推未来模型的印记以扩展训练数据，从而增强泛化能力和鲁棒性。此外，我们设计了一种新的处理流程，首次将基于噪声的印记提取器获得的噪声模式与其他视觉特征结合用于AI生成图像检测，显著提升了检测性能。我们的方法在包括GenImage、Synthbuster和Chameleon在内的三个公开基准测试中均达到了最先进的性能水平。