发表机构
Hochschule Darmstadt; Michigan State University; Inria Center at Université Côte d’Azur（达姆施塔特应用科学大学; 密歇根州立大学; 蔚蓝海岸大学INRIA中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有深度伪造数据集偏重良性内容且生成器过时的问题，构建了包含1840个真实与商业工具生成监控视频的CCDF数据集，评估表明现有最先进检测器无法可靠区分其生成视频。
AI 中文摘要
由于生成式人工智能的快速发展，商业视频生成工具可被用于制作伪造的监控录像，这些录像能够欺骗人类观众和自动化的合成视频检测器。由于这些工具易于获取，恶意用户能够以极低的成本创建有害的视频片段。在犯罪报告和选举等高风险场景中，此类视频的制作和传播可能误导应急响应工作或扭曲政治话语。研究界用于开发深度伪造检测算法的现有深度伪造视频数据集存在两个局限性：（1）它们侧重于良性网络内容，而非可能包含恶意活动的录像；（2）它们依赖于较旧或开源的生成器，这些生成器无法代表生成系统的最新进展。我们构建了CCtv DeepFakes（CCDF），一个视频深度伪造数据集，以解决上述两个不足。CCDF包含1840个视频（460个真实视频和1380个生成视频），涵盖16个犯罪和事故类别，生成内容使用三个领先的商业系统制作：Grok Imagine、Google VEO 3.1和OpenAI Sora 2。CCDF是一个高度逼真、小规模、人工标注的数据集，旨在评估检测模型。我们发布该数据集的三个版本：原始生成数据、清理版本（其中真实和合成样本之间的视频元数据被标准化，以防止检测器利用琐碎的线索），以及模拟低投入后处理攻击的修改版本。我们使用十个涵盖不同检测方法的最新最先进检测器评估CCDF。我们的结果表明，尽管这些方法在现有数据集上报告了强大的性能，但它们无法可靠地区分CCDF的生成视频与真实视频。这些结果进一步证实，现有数据集不适合评估某些威胁。
英文摘要
Due to rapid advances in Generative AI, commercial video generation tools can be used to produce fabricated surveillance footage that can fool both human viewers and automated synthetic video detectors. Since these tools are so widely accessible, a malicious user can create a harmful video clip at minimal cost. The production and dissemination of such videos in high-stakes settings, such as crime reporting and elections, can misdirect emergency response efforts or distort political discourse. Existing deepfake video datasets, used by the research community to develop deepfake detection algorithms, exhibit two limitations: (1) they emphasize benign web content rather than footage of possibly malicious activity, and (2) they rely on older or open-source generators that do not represent recent advances in generative systems. We assemble CCtv DeepFakes (CCDF), a video deepfake dataset, to address both gaps. CCDF contains 1840 videos (460 real and 1380 generated) spanning 16 crime and accident categories, with generated content produced using three leading commercial systems: Grok Imagine, Google VEO 3.1, and OpenAI Sora 2. CCDF is a highly realistic, small-scale, manually annotated dataset targeting evaluation of detection models. We release three versions of the dataset: the raw generated data, a cleaned version in which video metadata are standardized between real and synthetic samples to prevent detectors from exploiting trivial cues, and an altered version simulating low-effort post-processing attacks. We evaluate CCDF with ten recent state-of-the-art detectors covering different detection approaches. Our results suggest that these approaches do not reliably distinguish CCDF's generated videos from real ones, despite their strong reported performance on existing datasets. These results further confirm that existing datasets are not well-suited to evaluating certain threats.
