探测器失效之处：探索生成空间以实现可泛化的AI生成图像检测
Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection
AI总结：
提出PROBE框架，通过主动探索生成过程中的困难区域来改进AI生成图像检测器的泛化能力。
摘要：
检测AI生成图像（AIGI）仍然具有挑战性，因为检测器通常无法泛化到未见过的生成器。尽管现有方法在大数据集上训练，但当生成设置改变时，其性能仍然下降，这表明仅靠数据规模是不够的，训练期间生成变化的有限覆盖是一个关键因素。关于生成模型编辑的研究表明，内部表示的微小变化可以产生多样且有意义的图像变化，其中许多在标准采样下未被探索。利用这一见解，我们提出了PROBE（通过边界探索探测鲁棒性）框架，通过主动探索生成过程中的困难区域来改进检测器的泛化能力。PROBE不是将生成器视为固定的数据源，而是使用检测器作为批评者，通过流形级别的修改引导生成器，产生难以分类的真实样本。这些样本暴露了在标准数据采样策略下不常见的失败案例，并用于改进检测器。在多个基准上的实验结果表明，PROBE增强了对未见生成器的泛化能力，从而实现了更可泛化的AIGI检测性能。代码和模型可在 https://github.com/Amamiya-C/PROBE-AIGI-Detection 获取。
英文摘要：
Detecting AI-generated images (AIGI) remains challenging because detectors often fail to generalize to unseen generators. Although existing methods are trained on large datasets, their performance still degrades when generation settings change, indicating that data scale alone is insufficient and that limited coverage of generative variations during training is a key factor. Studies on generative model editing show that small changes in internal representations can produce diverse and meaningful image variations, many of which are not explored under standard sampling. Leveraging this insight, we propose PROBE (Probing Robustness via Boundary Exploration), a framework that improves detector generalization by actively exploring challenging regions of the generative process. Instead of treating the generator as a fixed data source, PROBE uses the detector as a critic to steer the generator through manifold-level modifications, producing realistic samples that are difficult to classify. These samples expose failure cases that are uncommon under standard data sampling strategies and are used to refine the detector. Experimental results across multiple benchmarks indicate that PROBE enhances generalization to unseen generators, resulting in more generalizable AIGI detection performance. Code and models are available at https://github.com/Amamiya-C/PROBE-AIGI-Detection
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
