Principles of Designing Robust Remote Face Anti-Spoofing Systems

TL;DR

This paper analyzes the vulnerabilities of current face anti-spoofing methods against digital attacks, proposes a taxonomy of threats, and offers design principles for more robust systems to counter emerging digital spoofing techniques.

Contribution

It provides a comprehensive threat taxonomy and introduces key design principles for developing face anti-spoofing systems resilient to digital attack vectors.

Findings

Current models fail against deepfake and adversarial attacks

Existing methods lack generalization to new digital attack scenarios

Proactive systems with active sensors can enhance robustness

Abstract

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.