Face Swapping in the Deepfake Era: Technologies, Applications, and Ethical Concerns

Abstract

The technology of face-swapping has developed from conventional manual editing to the emergence of deepfake technology powered by deep learning and then to the stage of diversified development, achieving improvements in terms of speed, realism and stability, and being applied to film, television, entertainment and virtual makeup, while also giving rise to problems such as false information and privacy violation attracting attention to the ethical and legal restrictions; among the mainstream models, GHOST, a deep-learning-based framework combining GANs and autoencoders, performs well in high-precision face-swapping by extracting and mapping facial features, keeping details and working stably under complex lighting but with large parameters and slow inference; Inswapper, an advanced pre-trained model trained on millions of diverse facial images, performs well in 3D facial structure inference and feature separation, making realistic transfers possible and being integrated into various tools flexibly but with potential blurring in results; SimSwap, a GAN-based framework with a generator and a discriminator, uses an ID Injection Module and weak feature loss for arbitrary swapping and target attribute keeping, although requiring considerable computing resources and possibly producing artifacts at extreme angles.