Deep Learning Models for Robust Facial Liveness Detection

TL;DR

This paper presents novel deep learning models that significantly improve facial liveness detection, effectively countering advanced spoofing attacks like deepfakes, with high accuracy demonstrated across multiple datasets.

Contribution

Introduces innovative deep learning models combining texture and reflective analysis to enhance anti-spoofing capabilities beyond existing methods.

Findings

Achieved 99.9% accuracy on combined datasets.

Models outperform current anti-spoofing techniques.

Provides insights into impostor attack behaviors.

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.