Face Authenticity: An Overview of Face Manipulation Generation, Detection and Recognition

Abstract

In recent years, there has been an exponential increase in photo and video manipulation by easy-to-use editing tools (e.g., Photoshop). Especially, ‘face digital manipulations’ (e.g., face swapping) is a critical issue for automated face recognition systems (AFRSs) as it detrimentally effects the AFRS’ performance. Also, the advent of powerful deep learning methods has led to realistic face sample generation and manipulation. Despite recent advances in face manipulation detection techniques, manipulations-aware AFRSs and face synthetic sample/manipulation generation, detecting sophisticated face manipulations is still a challenge to human examiners and existing technologies. Devising more effective universal face manipulation detectors and manipulations-aware AFRSs will immensely improve the trust in biometric applications and digital communications. This paper presents an overview of the recent technologies on face manipulation generation, detection, recognition, and databases. Also, potential future research directions and challenges are discussed.