Toward Deep Face Spoofing: Taxonomy, Recent Advances, and Open Challenges

Abstract

Deep neural networks are increasingly employed to create adversarial face images, aiming to deceive face recognition systems. While the majority of studies concentrate on digital attacks, their relevance extends to face spoofing. Notably, they have the capability to generate potential face images of victims when attackers lack knowledge about individuals registered in face recognition systems. Regrettably, recent advances in attacking face recognition systems using deep neural networks, their performance, and their transferability to physical attacks (deep face spoofing) lack systematic exploration. This paper addresses this gap by presenting the first comprehensive survey of current research in this domain. The review initiates with the definition of the deep face spoofing concept and introduces a pioneering taxonomy to systematically consolidate recent advances towards deep face spoofing. The main section of the paper provides in-depth evaluations of the mechanism, performance, and applicability of diverse deep neural network-based attacking algorithms against face recognition systems. Subsequently, the paper outlines current challenges in deep face spoofing, including the absence of evaluations of recent attacks against state-of-the-art face anti-spoofing algorithms and the limited transferability of recent digital attacks to physical attacks. This part also covers open challenges in deep face spoofing detection since it is crucial to note that studying various deep face spoofing algorithms should always be seen as an effort to investigate the vulnerability of face recognition systems against such evolved attacks, and not as an endeavor to gain access for illegal purposes. To enhance accessibility to a broad range of research papers in this area, an accompanying web page ( https://github.com/dhimasarief/DFS_DFAS ) has been established. This serves as a dynamic repository of studies focusing on deep face spoofing, continuously curated with new findings and contributions.