Deep Learning Models for Robust Facial Liveness Detection

2025-08-12

AI Summary

AI-driven spoofing attacks, particularly deepfakes, pose severe challenges to liveness detection in biometric authentication. Method: This paper proposes a novel deep learning architecture that jointly models the physical optical reflectance properties of human skin and local texture patterns. It employs a multimodal feature extraction network integrated with AttackNet V2.2 for end-to-end spoof detection and adopts cross-dataset joint training to enhance generalization. Contribution/Results: The key innovation lies in embedding human skin’s optical reflectance priors directly into the network architecture, thereby improving discriminative sensitivity to synthetic artifacts. Evaluated on five heterogeneous benchmark datasets, the method achieves a mean accuracy of 99.9%, substantially outperforming existing state-of-the-art approaches. This advancement significantly enhances the robustness and security of face recognition systems under complex adversarial conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Humans and AI: AI for Accessibility

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In the rapidly evolving landscape of digital security, biometric authentication systems, particularly facial recognition, have emerged as integral components of various security protocols. However, the reliability of these systems is compromised by sophisticated spoofing attacks, where imposters gain unauthorized access by falsifying biometric traits. Current literature reveals a concerning gap: existing liveness detection methodologies - designed to counteract these breaches - fall short against advanced spoofing tactics employing deepfakes and other artificial intelligence-driven manipulations. This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques. By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision. Extensive evaluations were conducted across five diverse datasets, encompassing a wide range of attack vectors and environmental conditions. Results demonstrate substantial advancement over existing systems, with our best model (AttackNet V2.2) achieving 99.9% average accuracy when trained on combined data. Moreover, our research unveils critical insights into the behavioral patterns of impostor attacks, contributing to a more nuanced understanding of their evolving nature. The implications are profound: our models do not merely fortify the authentication processes but also instill confidence in biometric systems across various sectors reliant on secure access.

Problem

• Detect facial spoofing attacks using deep learning
• Improve liveness detection against AI-driven manipulations
• Enhance biometric security with texture and reflection analysis

Innovation

• Deep learning models for facial liveness detection
• Texture and reflective properties integration
• High accuracy across diverse attack vectors