# Enhancing Mobile Privacy and Security: A Face Skin Patch-Based Anti-Spoofing Approach

arXiv: https://arxiv.org/abs/2308.04798 · HTML: https://mlsanity.com/paper/2308.04798/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Searching Central Difference Convolutional Networks for Face Anti-Spoofing](https://mlsanity.com/paper/2003.04092/) (2020)
- [Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision](https://mlsanity.com/paper/1803.11097/) (2018)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [face anti-spoofing based on color texture analysis](https://mlsanity.com/paper/1511.06316/) (2015)
- [End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation](https://mlsanity.com/paper/1910.14104/) (2019)
- [Structure Destruction and Content Combination for Face Anti-Spoofing](https://mlsanity.com/paper/2107.10628/) (2021)
- [Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture](https://mlsanity.com/paper/1901.05635/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Universal Railway Obstacle Detection System based on Semi-supervised Segmentation And Optical Flow](https://mlsanity.com/paper/2406.18908/) (2024)

## Abstract (arXiv)

As Facial Recognition System(FRS) is widely applied in areas such as access control and mobile payments due to its convenience and high accuracy. The security of facial recognition is also highly regarded. The Face anti-spoofing system(FAS) for face recognition is an important component used to enhance the security of face recognition systems. Traditional FAS used images containing identity information to detect spoofing traces, however there is a risk of privacy leakage during the transmission and storage of these images. Besides, the encryption and decryption of these privacy-sensitive data takes too long compared to inference time by FAS model. To address the above issues, we propose a face anti-spoofing algorithm based on facial skin patches leveraging pure facial skin patch images as input, which contain no privacy information, no encryption or decryption is needed for these images. We conduct experiments on several public datasets, the results prove that our algorithm has demonstrated superiority in both accuracy and speed.