首个评估AI生成图像解释质量的基准，揭示当前模型与人类判断仍有差距。
Explainable AI-Generated Image Detection RewardBench
- 用多模态大模型做裁判，评估其他模型对图像真伪解释的质量。
- 3000组标注数据测试显示最佳模型准确率仅88.76%，远低于人类98.30%。
- 揭示模型在解释判断中的常见错误，适合可解释AI与安全评测研究者参考。
传统基于分类的AI生成图像检测方法无法以人类专家可理解的方式解释图像为何被判定为真实或生成，降低了工具在现实应用中的可信度。利用多模态大语言模型（MLLM）已成为解决该问题的新兴方案。现有方法通常采用“MLLM作为裁判”来评估其他MLLM生成的解释质量，但这些裁判模型在评判自身或其他模型生成的解释时的表现尚未充分研究。为此，我们提出XAIGID-RewardBench，首个专门用于评估当前MLLM在判断图像真伪解释质量方面能力的基准。该基准包含约3000个标注三元组，数据来自多种图像生成模型及以MLLM为策略模型（检测器）的结果，用于评估当前MLLM作为奖励模型（裁判）的能力。结果显示，当前最佳奖励模型在该基准上得分仅为88.76%（而人类标注者间一致性达98.30%），表明现有MLLM的推理能力与人类水平仍存在显著差距。此外，我们还分析了模型常犯的典型错误。代码与数据集已开源于https://github.com/RewardBench/XAIGID-RewardBench。
原文摘要 · Abstract (English)
Conventional, classification-based AI-generated image detection methods cannot explain why an image is considered real or AI-generated in a way a human expert would, which reduces the trustworthiness and persuasiveness of these detection tools for real-world applications. Leveraging Multimodal Large Language Models (MLLMs) has recently become a trending solution to this issue. Further, to evaluate the quality of generated explanations, a common approach is to adopt an "MLLM as a judge" methodology to evaluate explanations generated by other MLLMs. However, how well those MLLMs perform when judging explanations for AI-generated image detection generated by themselves or other MLLMs has not been well studied. We therefore propose \textbf{XAIGID-RewardBench}, the first benchmark designed to evaluate the ability of current MLLMs to judge the quality of explanations about whether an image is real or AI-generated. The benchmark consists of approximately 3,000 annotated triplets sourced from various image generation models and MLLMs as policy models (detectors) to assess the capabilities of current MLLMs as reward models (judges). Our results show that the current best reward model scored 88.76\% on this benchmark (while human inter-annotator agreement reaches 98.30\%), demonstrating that a visible gap remains between the reasoning abilities of today's MLLMs and human-level performance. In addition, we provide an analysis of common pitfalls that these models frequently encounter. Code and benchmark are available at https://github.com/RewardBench/XAIGID-RewardBench.
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