# AttackNet: Enhancing Biometric Security via Tailored Convolutional Neural Network Architectures for Liveness Detection

arXiv: https://arxiv.org/abs/2402.03769 · HTML: https://mlsanity.com/paper/2402.03769/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [Learn Convolutional Neural Network for Face Anti-Spoofing](https://mlsanity.com/paper/1408.5601/) (2014)
- [Face De-Spoofing: Anti-Spoofing via Noise Modeling](https://mlsanity.com/paper/1807.09968/) (2018)
- [An Overview of Face Liveness Detection](https://mlsanity.com/paper/1405.2227/) (2014)
- [A Study on the Impact of Face Image Quality on Face Recognition in the Wild](https://mlsanity.com/paper/2307.02679/) (2023)

## Built on by (most cited first, 2 of 2)

- [Deep Learning Models for Robust Facial Liveness Detection](https://mlsanity.com/paper/2508.09094/) (2025)
- [MS-MFAD : Multimodal large language models for Face Anti-spoofing Detection](https://mlsanity.com/paper/2608.17328/) (2026)

## Abstract (arXiv)

Biometric security is the cornerstone of modern identity verification and authentication systems, where the integrity and reliability of biometric samples is of paramount importance. This paper introduces AttackNet, a bespoke Convolutional Neural Network architecture, meticulously designed to combat spoofing threats in biometric systems. Rooted in deep learning methodologies, this model offers a layered defense mechanism, seamlessly transitioning from low-level feature extraction to high-level pattern discernment. Three distinctive architectural phases form the crux of the model, each underpinned by judiciously chosen activation functions, normalization techniques, and dropout layers to ensure robustness and resilience against adversarial attacks. Benchmarking our model across diverse datasets affirms its prowess, showcasing superior performance metrics in comparison to contemporary models. Furthermore, a detailed comparative analysis accentuates the model's efficacy, drawing parallels with prevailing state-of-the-art methodologies. Through iterative refinement and an informed architectural strategy, AttackNet underscores the potential of deep learning in safeguarding the future of biometric security.