FakeParts: a New Family of AI-Generated DeepFakes
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.
68b12b7fb19c5400014849cc
Gaetan Brison
claimed_verified
2025-09-01T07:53:19.790Z
60ff033eaa025227d344b72f
https://cdn-avatars.huggingface.co/v1/production/uploads/60ff033eaa025227d344b72f/jZFoXssHmfxxhTxwpDbK3.png
Gaëtan Brison
user
gaetanbrison
68b12b7fb19c5400014849cd
Soobash Daiboo
68b12b7fb19c5400014849ce
Samy Aimeur
68b12b7fb19c5400014849cf
Awais Hussain Sani
68b12b7fb19c5400014849d0
Xi Wang
68b12b7fb19c5400014849d1
Gianni Franchi
68b12b7fb19c5400014849d2
Vicky Kalogeiton
FakeParts: a New Family of AI-Generated DeepFakes
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 introduces a new type of deepfake with subtle, localized manipulations that are difficult to detect, and FakePartsBench provides a large-scale dataset to evaluate detection methods.
deepfakes FakeParts partial manipulations facial expressions object substitutions background modifications FakePartsBench pixel-level frame-level manipulation annotations deepfake detection