SynCred-Bench: Benchmarking Synthetic Credibility in AI-Generated Visual Misinformation
For researchers and practitioners combating visual misinformation, this benchmark reveals the inadequacy of current detection systems against AI-generated credible-looking content.
The paper introduces SynCred-Bench, a benchmark of 600 AI-generated misinformation images with credible formats and circulation styles, plus a real-image negative set. Evaluation shows that under a 5% false-positive-rate constraint, 15 MLLMs achieve only 10.5% true positive rate, open-source detectors less than 5%, and commercial APIs 57.6%, while humans reach 63%, establishing synthetic credibility as a severe underexplored challenge.
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.