{"ID":3005091,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-05T07:50:16.0004273Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.03348","arxiv_id":"2606.03348","title":"SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation","abstract":"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.","short_abstract":"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 circulati...","url_abs":"https://arxiv.org/abs/2606.03348","url_pdf":"https://arxiv.org/pdf/2606.03348v1","authors":"[\"Junxiao Yang\",\"Minghao Zhang\",\"Xiaoce Wang\",\"Haoran Liu\",\"Shiyao Cui\",\"Hongning Wang\",\"Minlie Huang\"]","published":"2026-06-02T08:57:38Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Large Language Model\"]","has_code":false}