DFS-Net: StyleGAN2-based dual feature separation for face De-Morphing

Face morphing attacks pose a significant threat to face recognition systems. In order to solve this problem, several methods for detecting these attacks have been proposed. However, the restoration of the accomplice’s face image remains in its nascent developmental stage. In this paper, we introduce a novel network architecture termed DFS-Net, which leverages double feature spaces (latent code and feature tensor) and a dual-feature separation (DFS) network. DFS-Net is built upon the StyleGAN2 generator. By utilizing the feature vector and feature tensor extracted by the encoder, distinct separation networks are designed. This design facilitates effective feature separation, enabling DFS-Net to separate the identity features of accomplices. The incorporation of the feature tensor enhances the precision in extracting the identity features of the accomplice and thereby elevates the perceptual quality of the restoration image. Experimental results indicate that DFS-Net can effectively restore the accomplice’s face and outperforms previous works in terms of restoration accuracy and image visual quality.