Selective Domain-Invariant Feature for Generalizable Deepfake Detection
Yingxin Lai Guoqing Yang Yifan He Zhiming Luo Shaozi Li
With diverse presentation forgery methods emerging continually, detecting the
authenticity of images has drawn growing attention. Although existing methods
have achieved impressive accuracy in training dataset detection, they still
perform poorly in the unseen domain and suffer from forgery of irrelevant
information such as background and identity, affecting generalizability. To
solve this problem, we proposed a novel framework Selective Domain-Invariant
Feature (SDIF), which reduces the sensitivity to face forgery by fusing content
features and styles. Specifically, we first use a Farthest-Point Sampling (FPS)
training strategy to construct a task-relevant style sample representation
space for fusing with content features. Then, we propose a dynamic feature
extraction module to generate features with diverse styles to improve the
performance and effectiveness of the feature extractor. Finally, a domain
separation strategy is used to retain domain-related features to help
distinguish between real and fake faces. Both qualitative and quantitative
results in existing benchmarks and proposals demonstrate the effectiveness of
our approach.