# Deep Learning Models for Robust Facial Liveness Detection

arXiv: https://arxiv.org/abs/2508.09094 · HTML: https://mlsanity.com/paper/2508.09094/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [A Comparison of Optimization Algorithms for Deep Learning](https://mlsanity.com/paper/2007.14166/) (2020)
- [A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing](https://mlsanity.com/paper/1805.04176/) (2018)
- [AttackNet: Enhancing Biometric Security via Tailored Convolutional Neural Network Architectures for Liveness Detection](https://mlsanity.com/paper/2402.03769/) (2024)
- [Cross-Database Liveness Detection: Insights from Comparative Biometric Analysis](https://mlsanity.com/paper/2401.16232/) (2024)

## Abstract (arXiv)

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.