Frequency-spatial Interaction Generative Adversarial Network for Face De-morphing

The currently used face recognition systems have been proven to be seriously threatened by face morphing attacks. To counter this challenge, several detection methods have been proposed, with some focusing on restoring the accomplice's facial image. However, these methods are not yet mature enough to achieve high-quality restoration results. In this paper, we propose FSIFD-GAN, a face de-morphing method based on frequency-spatial dual-feature attention separation mechanism. In this method, frequency information is introduced into the face de-morphing research for the first time, and the task of separating accomplice's identity features from morphed images is transferred to frequency domain and spatial domain. FSIFD-GAN uses the attention mechanism to realize the separation operation of frequency and spatial features in two domains respectively, and then uses the frequency-spatial cross-attention operation to realize the interaction of frequency and spatial information, so as to obtain comprehensive accomplice's identity information. Our experiments demonstrate that FSIFD-GAN can effectively restore the accomplice's facial image. This can help to identify the accomplice's identity in criminal investigations and judicial evidence.