Selective domain-invariant feature for generalizable deepfake detection

Yingxin Lai, Guoqing Yang, Zhiming Luo, Shaozi Li

Abstract:

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.