Abstract
The rapid development of generative AI has made AI-generated images increasingly realistic and high-resolution. Most AI-generated image detection architectures typically downsample images before inputting them into models, risking the loss of fine-grained details. This paper presents GLASS (Global-Local Attention with Stratified Sampling), an architecture that combines a globally resized view with multiple randomly sampled local crops. These crops are original-resolution regions efficiently selected through spatially stratified sampling and aggregated using attention-based scoring. GLASS can be integrated into vision models to leverage both global and local information in images of any size. Vision Transformer, ResNet, and ConvNeXt models are used as backbones, and experiments show that GLASS outperforms standard transfer learning by achieving higher predictive performance within feasible computational constraints.
Abstract (translated)
生成式人工智能的快速发展使得AI生成的图像越来越逼真且分辨率更高。大多数用于检测AI生成图像的架构通常会在将图像输入模型之前对其进行降采样，这会带来细节损失的风险。本文提出了一种名为GLASS（分层抽样全局-局部注意力）的架构，该架构结合了全局重缩放视图与多个随机抽取的局部裁剪块。这些裁剪块是在空间分层抽样的基础上高效选择的原始分辨率区域，并通过基于注意力的评分机制进行聚合。GLASS可以集成到视觉模型中，以利用任何大小图像中的全局和局部信息。实验使用了Vision Transformer、ResNet 和 ConvNeXt 模型作为骨干网络，结果表明，在可行的计算约束下，GLASS能够超越标准迁移学习方法，实现更高的预测性能。
URL
https://arxiv.org/abs/2601.00141
