Research On High Resolution Face Replacement Technology

Deepfake technology specifically refers to the use of machine learning methods to enable computers to relatively quickly and easily generate multimedia content,such as false images,audio,and videos,targeting specific objectives.Facial replacement,as a branch of deepfake technology,plays an irreplaceable role in various areas,including entertainment,protecting specific individuals’ privacy,and facilitating communication for people with disabilities.It also holds a place in the field of national defense and security.Current facial replacement techniques still face challenges such as distortion of facial details and expression and generally low resolution.To address these issues,this paper first proposes a facial semantic information constraint algorithm and a facial latent vector editing algorithm,refining facial details and enhancing the similarity of pose and expression to the source face.Building upon this,the paper modifies and improves the facial replacement model based on Style GAN2,enhancing the resolution of generated facial images.(1)To address the issue of distorted facial expression,the proposed approach utilizes non-rigid deformation for the semantic information map of the target face,imbuing it with the motion and expression features of the source face.This solves the problem of significant differences between the generated facial expression and the source face.(2)In terms of facial texture and other attribute editing,an implicit vector editor for facial latent vectors is designed based on Style GAN2,enabling directional manipulation of the generated facial images and addressing the issue of strong randomness in image generation by GAN networks.(3)To tackle the common problem of low-resolution output in existing facial replacement networks,a facial replacement model based on Style GAN2 is modified and improved.This model consists of a facial information encoder,a facial attribute information editor,and a high-resolution facial generator.(4)Experimental results demonstrate that compared to traditional mainstream facial replacement methods,this approach achieves improvements of 1.15 in head pose similarity and 0.70 in expression similarity on the Flickr-Faces-High-Quality(FFHQ)dataset.Moreover,compared to existing high-resolution facial replacement methods,it achieves improvements of 0.71 and 0.15 in head similarity and 0.64 and 0.07 in expression similarity,respectively.