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

Research Multimodal & Generative

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Merged summary

TL;DR - RA-Bench evaluates AI-generated crisis-video detection across generators, detector families, human judgments, and social dissemination. Current detectors generalize inconsistently, especially on videos that deceive people or circulate socially.

  • Includes 17,886 videos spanning 10 social-risk categories, with 1,830 real anchors and 16,056 clips from nine generators.
  • Evaluates seven traditional detectors, ten zero-shot multimodal models, and two detection-fine-tuned MLLMs.
  • Generation quality, conditioning information, and sampling seeds affect detector performance differently.
  • Social dissemination reduces detection reliability, underscoring the need for more robust detectors.

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

arXiv cs.CV Shuo Liang, Yixing Ma, Pengfei Zhou, Xingyan Chen, Zihan Mei, Manting Li, Feihan Chen, Zhiwen Wang, Bin Xu, Haotian Zhang, Jiajun Song, Shiya Su, Run Liu, Zhenghang Ni, Yifa Yu, Jintao Hong, Bolong Feng, Yifei Liu, Zirui Zhang, Jingxuan Zhang, Songlin Zhao, Yifan Bai, Kang Tan, Yizhe Liu, Junhao Du, Yongtao Ge, Zhaopan Xv, Xinyuan Zhang, Mengru Ma, Chunhua Shen, Wei Wang, Yang You, Zheng Zhu, Kaipeng Zhang, Wangbo Zhao 2026-08-14 arXiv:2608.14391
Public signals Hugging Face upvotes 282
Providers: Hugging Face · Upvotes 282 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-15 14:32:44.109953 UTC

TL;DR - RA-Bench evaluates AI-generated crisis-video detection across generators, detector families, human judgments, and social dissemination. Current detectors generalize inconsistently, especially on videos that deceive people or circulate socially.

  • Includes 17,886 videos spanning 10 social-risk categories, with 1,830 real anchors and 16,056 clips from nine generators.
  • Evaluates seven traditional detectors, ten zero-shot multimodal models, and two detection-fine-tuned MLLMs.
  • Generation quality, conditioning information, and sampling seeds affect detector performance differently.
  • Social dissemination reduces detection reliability, underscoring the need for more robust detectors.
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