通过像素级扰动提升AI生成图像检测的泛化能力
Beyond Semantic Features: Pixel-level Mapping for Generalized AI-Generated Image Detection
- 在检测前对图像进行像素值分布扰乱，打破依赖特定语义线索的缺陷
- 在多种生成模型上显著提升检测器跨模型性能，最高提升18.7%准确率
- 适合需要跨模型通用检测能力的研究者和实际应用部署
生成技术的快速发展亟需可靠的AI生成图像检测方法。当前检测器普遍存在泛化能力差的问题，因其常过度依赖特定生成模型的语义特征，而非学习普遍存在的生成痕迹。为此，我们提出一种简单而有效的像素级映射预处理步骤，通过扰乱图像的像素值分布，破坏检测器常用的非必要语义模式。这迫使检测器关注图像生成过程中更基础、更具普适性的高频特征。在基于GAN与扩散模型的多种生成器上进行的全面实验表明，该方法显著提升了主流检测器的跨生成器性能。进一步分析验证了：破坏语义线索是实现泛化的核心机制。
原文摘要 · Abstract (English)
The rapid evolution of generative technologies necessitates reliable methods for detecting AI-generated images. A critical limitation of current detectors is their failure to generalize to images from unseen generative models, as they often overfit to source-specific semantic cues rather than learning universal generative artifacts. To overcome this, we introduce a simple yet remarkably effective pixel-level mapping pre-processing step to disrupt the pixel value distribution of images and break the fragile, non-essential semantic patterns that detectors commonly exploit as shortcuts. This forces the detector to focus on more fundamental and generalizable high-frequency traces inherent to the image generation process. Through comprehensive experiments on GAN and diffusion-based generators, we show that our approach significantly boosts the cross-generator performance of state-of-the-art detectors. Extensive analysis further verifies our hypothesis that the disruption of semantic cues is the key to generalization.
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