标题： FakeParts：一种新的AI生成的深度伪造技术 显示英文标题

标题： FakeParts: a New Family of AI-Generated DeepFakes

摘要： 我们引入了FakeParts，一种新的深度伪造类别，其特点是针对原本真实的视频的特定空间区域或时间片段进行细微的局部篡改。 与完全合成的内容不同，这些部分篡改，从改变面部表情到物体替换和背景修改，能够与真实元素无缝融合，使其特别具有欺骗性且难以检测。 为解决检测能力的关键缺口，我们提出了FakePartsBench，第一个大规模基准数据集，专门设计用于捕捉部分深度伪造的全范围。 该数据集包含超过25K个视频，并带有像素级和帧级的篡改标注，使检测方法的全面评估成为可能。 我们的用户研究显示，与传统深度伪造相比，FakeParts将人类检测准确率降低了30%以上，最先进的检测模型也观察到了类似的表现退化。 这项工作指出了当前深度伪造检测方法中的一个紧迫漏洞，并提供了开发更强大部分视频篡改检测方法所需的资源。 显示英文摘要

摘要： 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.