FakeParts：新一代AI生成深度伪造技术家族

摘要

我们推出FakeParts，这是一种新型深度伪造技术，其特点是对原本真实的视频进行细微、局部的空间区域或时间片段修改。与完全合成的内容不同，这些局部篡改——从改变面部表情到替换物体及修改背景——与真实元素无缝融合，使其极具欺骗性且难以识别。为填补检测能力的关键空白，我们提出了FakePartsBench，这是首个专门设计用于全面捕捉局部深度伪造的大规模基准数据集。该数据集包含超过25,000个视频，配有像素级和帧级篡改标注，为检测方法的全面评估提供了条件。我们的用户研究表明，与传统深度伪造相比，FakeParts使人类检测准确率降低了30%以上，同时在最先进的检测模型中也观察到了类似的性能下降。此研究揭示了当前深度伪造检测方法中的一个紧迫漏洞，并为开发针对局部视频篡改的更鲁棒方法提供了必要资源。

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 capabilities, we present FakePartsBench, the first large-scale benchmark dataset specifically designed to capture the full spectrum of partial deepfakes. Comprising over 25K videos with pixel-level and frame-level manipulation annotations, our dataset enables comprehensive evaluation of detection methods. Our user studies demonstrate that FakeParts reduces human detection accuracy by over 30% compared to traditional deepfakes, with similar performance degradation observed in state-of-the-art detection models. This work identifies an urgent vulnerability in current deepfake detection approaches and provides the necessary resources to develop more robust methods for partial video manipulations.

FakeParts：新一代AI生成深度伪造技术家族

FakeParts: a New Family of AI-Generated DeepFakes

摘要

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