构建合成可信度评测基准，揭示AI生成假信息的隐蔽威胁
SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation
- 设计600张平衡分布的AI伪造图像，覆盖六类可信形式与七种传播风格
- 现有模型在5%误报率下真阳性率最高仅57.6%，人类识别率也仅63%
- 提供真实图像负样本集FP450，助力检测器突破表面可信线索
当前生成模型可产出带真实文本和排版的视觉伪像，催生新型误导信息威胁：合成可信度。我们提出SYNCRED-Bench，包含600张均衡分布于六类可信形式与七种细粒度传播风格的AI生成误导图像，并引入FP450真实图像负样本集以衡量误报率。大量评估显示，现有系统仍不可靠：在5%误报率约束下，15个MLLM的真阳性率（TPR）仅为10.5%，开源AIGC检测器低于5%，商业API达到57.6%。人工标注者识别合成可信度的表现也仅达63% TPR。这些发现确立了合成可信度为严重且未被充分研究的视觉误导挑战，并为开发超越表层可信线索的检测方法提供了基准。
原文摘要 · Abstract (English)
Recent generative models can now produce visual artifacts with realistic embedded text and layouts, creating a new misinformation threat: synthetic credibility. We introduce SYNCRED-Bench, a benchmark of 600 AI-generated misinformation images balanced across six credible-form categories and seven fine-grained circulation styles, together with FP450, a real-image negative set for measuring false positives. Extensive evaluation shows that existing systems remain unreliable: under a 5% false-positive-rate constraint, 15 MLLMs achieve only 10.5% true positive rate (TPR), open-source AIGC detectors achieve less than 5%, and commercial APIs reach 57.6%. Human annotators also struggled to identify synthetic credibility, reaching only 63% TPR. These findings establish synthetic credibility as a severe and underexplored visual misinformation challenge, and provide a benchmark for developing detectors that reason beyond superficial credibility cues.
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