提出新型局部伪造视频FakeParts，更难被识别。
FakeParts: a New Family of AI-Generated DeepFakes
- 通过局部区域或时间段篡改真实视频，如换脸、换物、改背景。
- 构建81K视频数据集，含44K局部伪造样本，带像素级标注。
- 人类和先进模型检测准确率下降26%，揭示现有检测系统漏洞。
我们提出FakeParts，一种新型深度伪造视频，其特征是对真实视频的特定空间区域或时间片段进行细微、局部的篡改。与完全合成内容不同，这些部分篡改——包括面部表情改变、物体替换和背景修改——能与真实元素无缝融合，极具欺骗性且难以检测。为填补检测领域的空白，我们提出了FakePartsBench，首个专门针对局部深度伪造的大型基准数据集。该数据集包含超过81,000个视频（其中44,000个为FakeParts），并提供像素级和帧级篡改标注，支持对检测方法的全面评估。用户研究表明，与传统深度伪造相比，FakeParts使人类检测准确率降低高达26%，且先进检测模型也出现类似性能下降。本工作揭示了当前检测系统在应对局部篡改时的重大脆弱性，并提供了必要资源以开发更具鲁棒性的检测方法。
原文摘要 · Abstract (English)
We introduce FakeParts, a new class of deepfakes characterized by subtle, localized manipulations to specific spatial regions or temporal segments of otherwise authentic videos. Unlike fully synthetic content, these partial manipulations - ranging from altered facial expressions to object substitutions and background modifications - blend seamlessly with real elements, making them particularly deceptive and difficult to detect. To address the critical gap in detection, we present FakePartsBench, the first large-scale benchmark specifically designed to capture the full spectrum of partial deepfakes. Comprising over 81K (including 44K FakeParts) videos with pixel- and frame-level manipulation annotations, our dataset enables comprehensive evaluation of detection methods. Our user studies demonstrate that FakeParts reduces human detection accuracy by up to 26% compared to traditional deepfakes, with similar performance degradation observed in state-of-the-art detection models. This work identifies an urgent vulnerability in current detectors and provides the necessary resources to develop methods robust to partial manipulations.
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