Masked Conditional Diffusion Model for Enhancing Deepfake Detection
Tiewen Chen Shanmin Yang Shu Hu Zhenghan Fang Ying Fu Xi Wu Xin Wang
Recent studies on deepfake detection have achieved promising results when
training and testing faces are from the same dataset. However, their results
severely degrade when confronted with forged samples that the model has not yet
seen during training. In this paper, deepfake data to help detect deepfakes.
this paper present we put a new insight into diffusion model-based data
augmentation, and propose a Masked Conditional Diffusion Model (MCDM) for
enhancing deepfake detection. It generates a variety of forged faces from a
masked pristine one, encouraging the deepfake detection model to learn generic
and robust representations without overfitting to special artifacts. Extensive
experiments demonstrate that forgery images generated with our method are of
high quality and helpful to improve the performance of deepfake detection
models.