FakeParts: a New Family of AI-Generated DeepFakes
Authors: name: Gaetan Brison, name: Soobash Daiboo, name: Samy Aimeur, name: Awais Hussain Sani, name: Xi Wang, name: Gianni Franchi, name: Vicky Kalogeiton

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.