  Protecting digital identities of human face from various attack vectors is
paramount, and face anti-spoofing plays a crucial role in this endeavor.
Current approaches primarily focus on detecting spoofing attempts within
individual frames to detect presentation attacks. However, the emergence of
hyper-realistic generative models capable of real-time operation has heightened
the risk of digitally generated attacks. In light of these evolving threats,
this paper aims to address two key aspects. First, it sheds light on the
vulnerabilities of state-of-the-art face anti-spoofing methods against digital
attacks. Second, it presents a comprehensive taxonomy of common threats
encountered in face anti-spoofing systems. Through a series of experiments, we
demonstrate the limitations of current face anti-spoofing detection techniques
and their failure to generalize to novel digital attack scenarios. Notably, the
existing models struggle with digital injection attacks including adversarial
noise, realistic deepfake attacks, and digital replay attacks. To aid in the
design and implementation of robust face anti-spoofing systems resilient to
these emerging vulnerabilities, the paper proposes key design principles from
model accuracy and robustness to pipeline robustness and even platform
robustness. Especially, we suggest to implement the proactive face
anti-spoofing system using active sensors to significant reduce the risks for
unseen attack vectors and improve the user experience.
