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 NIRNet (Noise-based Imprint Revealing Network), a novel framework that leverages noise-based 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 imprint from various generative models, imprint 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, significantly improving detection performance. Our approach achieves state-of-the-art performance across seven diverse benchmarks, including five public datasets and two newly proposed generalization tests, demonstrating its superior generalization and effectiveness. Paper Submission: pdf

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。现有方法因泛化能力不足，在新兴生成模型上表现欠佳。为解决这一问题，本文提出NIRNet（基于噪声的印记揭示网络），一种利用噪声印记进行检测任务的新颖框架。具体而言，我们提出一种新颖的基于噪声的印记模拟器，以捕获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流程，率先使用从基于噪声的印记提取器导出的噪声模式，以及其他视觉特征进行AI生成图像检测，显著提升了检测性能。我们的方法在七个多样化基准测试中实现了最先进的性能，包括五个公共数据集和两个新提出的泛化测试，证明了其卓越的泛化能力和有效性。