FakeParts：一個全新的人工智慧生成深度偽造系列

摘要

我們介紹了FakeParts，這是一種新型的深度偽造技術，其特徵在於對原本真實的視頻進行細微且局部的修改，這些修改僅針對特定的空間區域或時間片段。與完全合成的內容不同，這些局部操縱——從改變面部表情到替換物體及修改背景——與真實元素無縫融合，使其特別具有欺騙性且難以檢測。為應對檢測能力上的這一關鍵缺口，我們提出了FakePartsBench，這是首個專門設計用於捕捉所有類型局部深度偽造的大規模基準數據集。該數據集包含超過25,000個視頻，並提供像素級和幀級的操作註釋，從而支持對檢測方法的全面評估。我們的用戶研究表明，與傳統深度偽造相比，FakeParts使人類檢測準確率降低了超過30%，並且在最先進的檢測模型中也觀察到了類似的性能下降。這項工作揭示了當前深度偽造檢測方法中的一個緊迫漏洞，並為開發更強大的局部視頻操縱檢測方法提供了必要的資源。

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：一個全新的人工智慧生成深度偽造系列

FakeParts: a New Family of AI-Generated DeepFakes

摘要

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