Current AI-Generated Image (AIGI) detection approaches predominantly rely on binary classification to distinguish real from synthetic images, often lacking interpretable or convincing evidence to substantiate their decisions. This limitation stems from existing AIGI detection benchmarks, which, despite featuring a broad collection of synthetic images, remain restricted in their coverage of artifact diversity and lack detailed, localized annotations. To bridge this gap, we introduce a fine-grained benchmark towards eXplainable AI-Generated image Detection, named X-AIGD, which provides pixel-level, categorized annotations of perceptual artifacts, spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals. These comprehensive annotations facilitate fine-grained interpretability evaluation and deeper insight into model decision-making processes. Our extensive investigation using X-AIGD provides several key insights: (1) Existing AIGI detectors demonstrate negligible reliance on perceptual artifacts, even at the most basic distortion level. (2) While AIGI detectors can be trained to identify specific artifacts, they still substantially base their judgment on uninterpretable features. (3) Explicitly aligning model attention with artifact regions can increase the interpretability and generalization of detectors. The data and code are available at: https://github.com/Coxy7/X-AIGD.

翻译：当前AI生成图像检测方法主要依赖二元分类来区分真实与合成图像，通常缺乏可解释或令人信服的证据来支撑其判断。这一局限源于现有AIGI检测基准：尽管收录了大量合成图像，但在伪影多样性覆盖方面仍显不足，且缺乏详细的局部标注。为弥补这一空白，我们提出了面向可解释AI生成图像检测的细粒度基准X-AIGD，该基准提供像素级、分类化的感知伪影标注，涵盖低层失真、高层语义及认知层反事实三个维度。这些综合性标注有助于开展细粒度可解释性评估，并深入洞察模型决策过程。基于X-AIGD的广泛实验得出以下关键发现：（1）现有AIGI检测器对感知伪影的依赖程度可忽略不计，即使在最基础的失真层面亦然。（2）虽然AIGI检测器可通过训练识别特定伪影，但其判断仍主要基于不可解释特征。（3）显式对齐模型注意力与伪影区域能有效提升检测器的可解释性与泛化能力。数据与代码已开源：https://github.com/Coxy7/X-AIGD。