Summary of A Machine Learning-based Secure Face Verification Scheme and Its Applications to Digital Surveillance, by Huan-chih Wang and Ja-ling Wu
A Machine Learning-Based Secure Face Verification Scheme and Its Applications to Digital Surveillance
by Huan-Chih Wang, Ja-Ling Wu
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a secure face verification system that protects facial images from being imitated. They use the DeepID2 convolutional neural network to extract features and an expectation-maximization (EM) algorithm to solve the verification problem. To maintain privacy, they apply homomorphic encryption schemes to encrypt data and compute the EM algorithm in the ciphertext domain. The paper presents three face verification systems for surveillance control, demonstrating their feasibility for practical implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to create a secure way to recognize people using facial images. They developed a system that uses special computer networks (DeepID2) and math algorithms (EM) to identify people. To keep the images private, they added extra security measures (homomorphic encryption). The researchers tested their system with three different levels of privacy protection and found it works well. |
Keywords
» Artificial intelligence » Neural network
