超越最后一层：面向AI生成图像检测的全局层间聚合
原标题：Beyond the Last Layer: Global Inter-Layer Aggregation for AI-Generated Image Detection
AI 摘要
论文提出 GILA（全局层间聚合）框架，用于 AI 生成图像检测。该方法从冻结预训练视觉编码器的不同深度提取全局表示，通过层间自注意力建模各层表示间的交互，并自适应聚合上下文表示，无需微调编码器。在三种视觉骨干、十个已知生成器和三个外部数据集上的实验表明，GILA 更好利用了多层信息，提升了检测性能和跨数据集泛化能力。
正文节选
Beyond the Last Layer: Global Inter-Layer Aggregation for AI-Generated Image Detection Abstract The rapid development of generative models has made AI-generated images increasingly close to real images. It has also brought several challenges to detectors, including rapid generator updates, large changes in data distribution, and limited cross-dataset generalization. Many existing methods based on pretrained vision encoders primarily use the global representation from the last layer, potentially