我们能否防御AI生成视频对现实世界危机事件的攻击？对检测器、生成器及社交传播的系统性评估
Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination
浏览论文内容
中文总结 AI 辅助
本研究推出RA-Bench基准，系统评估AI生成视频检测器、生成器及社交传播特性，发现现有检测器泛化性差、难以检测逼真生成视频，需鲁棒检测器。
中文摘要 AI 辅助
近期的视频生成器能够生成关于战争、灾害、公共紧急事件及其他现实危机的逼真虚假内容，带来了重大的错误信息风险。然而，现有基准测试并未充分提供此类场景下检测器和生成器行为的证据，包括可检测性如何随生成条件变化、人们对生成视频的感知情况，以及检测器在社交传播期间是否保持可靠。为解决这一缺口，我们推出RA-Bench——一种以真实视频为锚点的AI生成视频检测基准。RA-Bench包含17886个视频，涵盖10类社交风险的1830个真实视频锚点，以及来自4个开源和5个闭源生成器的16056个生成片段。基于RA-Bench，我们沿三个维度组织评估：首先，评估7种传统检测器、3种评测设置下的10种零样本多模态模型，以及2种专门针对AI生成视频检测微调的多模态大语言模型（MLLM）的泛化能力，结果显示三类检测器均未在RA-Bench样本上表现出一致的泛化性；其次，研究可检测性如何随生成质量、条件信息和采样种子变化，分析表明生成特性对不同检测器家族的影响存在差异，而源级检测模式在不同种子间保持稳定；最后，探究社交传播期间的人类真实性判断和检测器可靠性，发现误导人们的视频对当前检测器也难以检测，且社交传播会使检测难度进一步提升。综合来看，这些研究结果表明，当前方法难以检测逼真的AI生成视频，凸显了对可应对不断发展的视频生成器的鲁棒检测器的需求。
英文摘要
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 during social dissemination. To address this gap, we introduce RA-Bench, a benchmark for AI-generated video detection that uses Real videos as Anchors. RA-Bench contains 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 on RA-Bench, we organize our evaluation along three dimensions. We first assess detector generalization across seven traditional detectors, ten zero-shot multimodal models under three review settings, and two MLLMs specifically fine-tuned on AI-generated video detection. Across these methods, none of the three detector families generalizes consistently across RA-Bench instances. 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 during social dissemination. We find that videos that mislead people are also difficult for current detectors, and that social dissemination makes 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.
