With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this problem suffer from poor generalization across domains and generative models. In this work, we focus on the robustness of AI-generated image (AIGI) detectors. We analyze existing state-of-the-art AIGI detection methods based on frozen CLIP embeddings and show how to interpret them, shedding light on how images produced by various AI generators differ from real ones. Next we propose two ways to improve robustness: based on removing harmful components of the embedding vector and based on selecting the best performing attention heads in the image encoder model. Our methods increase the mean out-of-distribution (OOD) classification score by up to 6% for cross-model transfer. We also propose a new dataset for AIGI detection and use it in our evaluation; we believe this dataset will help boost further research. The dataset and code are provided as a supplement.

翻译：随着生成模型能力的不断增强，人工内容检测已成为日益重要且困难的任务。然而，该领域所有主流方法均存在跨领域与跨生成模型泛化能力不足的问题。本研究聚焦于AI生成图像检测器的鲁棒性。我们分析了基于冻结CLIP嵌入的现有最先进AIGI检测方法，阐释了其可解释性，揭示了各类AI生成器所产图像与真实图像的差异机制。进而提出两种提升鲁棒性的方案：基于嵌入向量有害成分消除的方法，以及基于图像编码器模型中最佳注意力头选择的方法。在跨模型迁移任务中，我们的方法将分布外分类平均准确率最高提升6%。同时构建了全新的AIGI检测数据集并用于评估验证，该数据集有望推动后续研究发展。数据集与代码已作为补充材料提供。