Paper page - Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination

Source: https://huggingface.co/papers/2608.14391 Published on Aug 14

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Abstract

A new benchmark for AI-generated video detection reveals that current detectors fail to generalize across realistic crisis-related videos and become less reliable as content spreads socially.

Recent video generators can fabricate realistic depictions of wars, disasters, public emergencies, and other real-world crises, creating substantial risks of misinformation. Existing benchmarks, however, provide limited evidence on detector and generator behavior in such settings, including how detectability varies with generation conditions, how people perceive generated videos, and whether detectors remain reliable duringsocial dissemination. To address this gap, we introduceRA-Bench, a benchmark forAI-generated video detectionthat uses Real videos as Anchors.RA-Benchcontains 17,886 videos, comprising 1,830 real-video anchors across 10 social-risk categories and 16,056 generated clips from four open-source and five closed-source generators. Based onRA-Bench, we organize our evaluation along three dimensions. We first assessdetector generalizationacross seven traditional detectors, tenzero-shot multimodal modelsunder three review settings, and twoMLLMsspecifically fine-tuned onAI-generated video detection. Across these methods, none of the three detector families generalizes consistently acrossRA-Benchinstances. We then examine how detectability varies with generation quality, conditioning information, and sampling seeds. These analyses show that generation properties affect detector families differently, while source-level detection patterns remain stable across seeds. Finally, we study human authenticity judgments and detector reliability duringsocial dissemination. We find that videos that mislead people are also difficult for current detectors, and thatsocial disseminationmakes detection harder. Together, these findings show that current methods struggle to detect realistic AI-generated videos, highlighting the need for detectors robust to evolving video generators.

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