发表机构
School of Cyber Science and Engineering, Southeast University; Purple Mountain Laboratories; School of Computer Science and Engineering, Southeast University（东南大学网络空间科学与工程学院; 紫金山实验室; 东南大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出RAID方法，通过位反转图像构建、梯度块选择与卷积分类器实现AI生成图像检测，在40多个基准上优于现有方法且速度快约100倍，还提供了理论分析与新数据集。
AI 中文摘要
图像生成模型的快速发展使得人们越来越难以区分AI生成图像与真实图像。为防止伪造图像滥用带来的潜在风险，AI生成图像检测受到了广泛关注。现有方法忽略了真实图像与伪造图像之间的固有差异，因此缺乏鲁棒性和泛化能力。本研究创新性地利用位平面研究AI生成图像检测，并引入了位反转图像。我们提出了一种简单却有效的流程，包括位反转图像构建、基于梯度的块选择和卷积分类器。此外，我们从数学角度提供了理论分析以证明方法的有效性，还引入了两个具有挑战性的AI生成图像检测数据集。大量实验验证了我们的方法在不同设置下的有效性，包括跨生成器泛化、跨数据集泛化和零样本性能。未引入额外复杂组件的情况下，我们的方法在40多个基准测试中优于现有方法，且速度约为同类方法的100倍。代码可在指定URL获取。
英文摘要
The rapid advancement of image generation models has made it increasingly difficult for people to distinguish AI-generated images from real ones. To prevent the potential risks associated with the misuse of fake images, AI-generated image detection has gained significant attention. Existing methods neglect the inherent differences between real and fake images, thus lacking robustness and generalization ability. In this work, we innovatively investigate AI-generated image detection using bit-planes, and introduce the bit-reversed image. We propose a simple yet effective pipeline consisting of construction of bit-reversed images, gradient-based patch selection and a convolutional classifier. Besides, we provide a theoretical analysis from the mathematical perspective to demonstrate the validity of our approach. We also introduce two challenging datasets for AI-generated image detection. Extensive experiments verify the effectiveness of our approach across different settings, including cross-generator generalization, cross-dataset generalization and zero-shot performance. Without bells and whistles, our approach outperforms existing methods on over 40 benchmarks, and is nearly 100 times faster than counterparts. The code is at https://github.com/renxi-seu/RAID.
Comments14 pages, 6 figures
