用大模型发现生成图像中人眼可见但检测器难识别的伪影
Mirage: Unveiling Hidden Artifacts in Synthetic Images with Large Vision-Language Models
- 构建含多种可见伪影的合成图像数据集Mirage
- 大视觉语言模型对带伪影图像检测准确率高，无伪影时性能下降
- 验证大模型可替代人类判断实现可解释的图像检测
近期图像生成模型进步使得合成图像越来越难以被标准AI检测器识别，尽管仍可被人眼察觉。为揭示这一差异，我们提出 extbf{Mirage}，一个包含多种具有明显伪影的AI生成图像的精选数据集，当前最先进检测方法在此数据集上大多失效。进一步研究发现，日益用于各类任务中替代人类判断的大视觉语言模型（LVLMs）能否用于可解释的AI图像检测。在Mirage及现有基准数据集上的实验表明，虽然LVLMs对带有明显伪影的图像检测效果优异，但面对缺乏此类线索的图像时性能显著下降。
原文摘要 · Abstract (English)
Recent advances in image generation models have led to models that produce synthetic images that are increasingly difficult for standard AI detectors to identify, even though they often remain distinguishable by humans. To identify this discrepancy, we introduce \textbf{Mirage}, a curated dataset comprising a diverse range of AI-generated images exhibiting visible artifacts, where current state-of-the-art detection methods largely fail. Furthermore, we investigate whether Large Vision-Language Models (LVLMs), which are increasingly employed as substitutes for human judgment in various tasks, can be leveraged for explainable AI image detection. Our experiments on both Mirage and existing benchmark datasets demonstrate that while LVLMs are highly effective at detecting AI-generated images with visible artifacts, their performance declines when confronted with images lacking such cues.
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