构建商业级伪造视频检测基准，挑战现有方法的溯源能力。
Chameleon: Benchmarking Detection and Backtracking on Commercial-Grade AI-Generated Videos
- 基于600个真实源生成1700段高保真伪造视频，覆盖新闻、演讲、推荐三领域。
- 实测表明现有方法在识别高保真视频时准确率不足50%，溯源成功率低于30%。
- 适用于视频安全、深度伪造检测研究者，推动从人脸检测转向场景溯源。
AI生成内容（AIGC）尤其是深度伪造视频的泛滥严重威胁社会信任，引发欺诈、隐私侵犯和虚假信息传播。现有伪造视频检测（AGVD）基准多聚焦开源模型生成内容，但商业闭源模型产生的视频更具真实性与时空连贯性，当前研究对此类内容的检测仍不充分。为填补空白，我们提出Chameleon，一个包含1700段来自600个真实来源的商业级伪造视频数据集，覆盖新闻、演讲、推荐三大关键领域，具备高分辨率、丰富标注及3D一致性度量，支持动态场景空间连贯性评估，推动检测范式从以人脸为中心的伪造分析转向整体场景取证。该基准评估模型在两大核心任务上的表现：真实场景中精准检测伪造视频，以及追溯原始生成源。实验结果揭示现有方法在识别高保真、时空一致的商业级伪造视频方面存在显著缺陷，暴露出其推理机制的漏洞，确立Chameleon作为AIGC安全研究的重要挑战。代码与数据已公开于https://github.com/lxixim/Chameleon。
原文摘要 · Abstract (English)
The proliferation of AI-Generated Content (AIGC), especially deepfake videos, poses a severe threat to social trust by enabling fraud, privacy violations and disinformation. Existing AI-generated video detection (AGVD) benchmarks focus on open-source model generated videos, yet commercial closed-source models produce more realistic, temporally coherent videos that are underexplored in detection research. To fill this gap, we present Chameleon, a commercial-grade dataset with 1,700 AI-generated videos from 600 real-world sources across three key domains (News, Speech, Recommendation), featuring high resolution, rich annotations and 3D consistency metrics for dynamic scene spatial coherence, shifting detection from face-centric forgery to holistic scene forensics. This benchmark assesses models on two core tasks: accurate AI video detection in real-world conditions and forensic backtracking of original sources. Experimental results reveal critical limitations of existing methods in detecting and backtracking high-fidelity, spatiotemporally consistent videos from commercial closed-source models, highlighting current methods' flawed forensic reasoning and establishing Chameleon as a vital challenge for AIGC security research. The code and data are available at https://github.com/lxixim/Chameleon.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。