Improving multimedia information security by enriching face antispoofing dataset with a facial forgery method

Abstract

The development of artificial intelligence, especially the application and development of deep learning, brings not only great convenience to people but also some challenges. Some face forgery techniques can confuse the false with the true in deep learning. With the widespread popularity of social applications and streaming media, people pay more and more attention to portrait privacy and security. To solve the great threat to personal privacy caused by deep face forgery technology, we propose an effective face forgery method to enrich the face antispoofing datasets. We first explore the representation of identity information in latent space. Based on this foundation, we use the identity feature replacement module to edit the latent codes in the latent space and finally generate the images with photo-realistic results with the help of the powerful generation ability of StyleGAN. The face forgery method can further expand the face antispoofing dataset, provide rich data for the learning-based forgery detection methods to improve their generalization and to ensure the security in preserving portrait privacy.