利用生成器末尾组件提升对未知AI图像的检测能力
Exploiting the Final Component of Generator Architectures for AI-Generated Image Detection
- 用生成器末尾组件污染真实图像，训练检测器区分真伪
- 仅用每类100样本，跨22个未见生成器平均准确率达98.83%
- 提出生成器末尾组件分类体系，适合通用检测场景
随着强大图像生成模型的快速普及，准确检测AI生成图像已成为维护可信网络环境的关键。然而，现有深度伪造检测器在面对未见过的生成器时泛化能力差。值得注意的是，尽管采用不同训练范式（如扩散或自回归建模），许多现代图像生成器共享相同的最终架构组件，该组件负责将中间表示转换为图像。受此启发，我们提出使用生成器的最终组件对真实图像进行“污染”，并训练检测器以区分被污染图像与原始真实图像。我们进一步基于生成器的最终组件构建分类体系，对21种广泛使用的生成器进行归类，从而全面评估该方法的泛化能力。仅使用三类代表性生成器各100个样本，经DINOv3主干网络微调的检测器，在22个来自未见生成器的测试集上平均准确率达到98.83%。
原文摘要 · Abstract (English)
With the rapid proliferation of powerful image generators, accurate detection of AI-generated images has become essential for maintaining a trustworthy online environment. However, existing deepfake detectors often generalize poorly to images produced by unseen generators. Notably, despite being trained under vastly different paradigms, such as diffusion or autoregressive modeling, many modern image generators share common final architectural components that serve as the last stage for converting intermediate representations into images. Motivated by this insight, we propose to "contaminate" real images using the generator's final component and train a detector to distinguish them from the original real images. We further introduce a taxonomy based on generators' final components and categorize 21 widely used generators accordingly, enabling a comprehensive investigation of our method's generalization capability. Using only 100 samples from each of three representative categories, our detector-fine-tuned on the DINOv3 backbone-achieves an average accuracy of 98.83% across 22 testing sets from unseen generators.
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