Principles of Designing Robust Remote Face Anti-Spoofing Systems

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Authors: Xiang Xu, Tianchen Zhao, Zheng Zhang, Zhihua Li, Jon Wu, Alessandro Achille, Mani Srivastava

Published: 2024-06-06 02:05:35+00:00

AI Summary

This paper analyzes vulnerabilities of state-of-the-art face anti-spoofing methods against digital attacks (deepfakes, adversarial noise, replay attacks). It proposes key design principles for robust remote face anti-spoofing systems, advocating a proactive approach using active sensors to mitigate emerging threats and improve user experience.

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.

Key findings

Existing single-frame passive face anti-spoofing models are highly vulnerable to various attacks. A proactive approach using active sensors is crucial for robust systems. Training models on diverse datasets covering various attacks is necessary for better generalization and robustness.

Approach

The paper evaluates existing face anti-spoofing models' performance against various attacks (presentation attacks, deepfakes, adversarial attacks, replay attacks) using public datasets. Based on the findings, it proposes design principles for robust systems, emphasizing a proactive approach leveraging active sensors to prevent unseen attacks.

Datasets

SiW, OULU-NPU, DeeperForensics, FaceForensics++, CASIA-MFSD, MSU-MFSD, IDIAP-Replay, CelebA-Spoof, CASIA-SURF 3DMask, CASIA-SURF CeFA, CASIA-SURF HiFiMask, CASIA-SURF SuHiFiMask, DeepfakeTIMIT, FaceForensics++, Celeb-DF, DFDC Preview, DFDC, DeeperForensics, WildDeepfake, FakeAVCeleb, FFIW, UniAttackData

Model(s)

CDCN, MDFAS, SAFAS, CADDM, ResNet-50 based models (R50-OULU, R50-FF, R50-ONLU-FF, R50-ONLU-FF-FUSE), DPNet, MADD, I2G, Joint, Self-supervised, Implicit-ID, ID-unaware, UCF, Anomaly, SFDG, AltFreezing

Author countries

USA

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