A Design Method of Secure Face Authentication System Based on CNN Feature Extractor

Abstract

A design method for a secure face authentication system based on a CNN feature extractor belongs to the field of biometric identification, and specifically relates to a method for extracting face features using CNN and encrypting them with Paillier algorithm and inadvertent transmission protocol. Compared with the SCiFi system, the present invention replaces manually extracted features with features learned automatically by CNN, and performs binarization to remove the influence of noise, and has higher authentication accuracy. The test authentication rate on LFW library view2 is 91.48%. During the entire authentication process, the server will not know any characteristic information of the requester, and can only receive the characteristic ciphertext information but cannot decrypt it. The client only knows whether the authentication is passed, and knows nothing about other information including the Hamming distance. This system uses 320bit features to represent a face picture, and the amount of feature data is reduced by 2 / 3 compared with the SCiFi system, so the time-consuming encryption and authentication is low and the real-time performance is high.

Description

technical field [0001] The invention belongs to the field of biological feature identification, relates to biological feature authentication and encryption technology, and in particular to a method for extracting face features by CNN and encrypting them with Paillier algorithm and inadvertent transmission protocol. Background technique [0002] In the field of biometric identification, the face has broad application prospects due to its contact-free and natural identification methods, including identity authentication in documents, security monitoring and monitoring, and network security control. However, the traditional biometric identification system does not have any encryption measures when storing. Once the features in the database are stolen, it means the leakage of private information, and there are great security and privacy risks. And the biometric feature is irrevocable and cannot be reset many times, so it is of great application significance to study a high authe...

AI Technical Summary

Problems solved by technology

Before 2006, most of the features in face authentication algorithms were artificially defined, such as LBP, SIFT and Gabor, etc., and the accuracy of authentication algorithms based on these features was also limited.

However, the authentication rate is low, the program takes a long time to run, and the article "Reconstructing a fragmented face from a cryptographic identification protocol" proves that the SCiFi system is vulnerable to the method of reconstructing fragmented faces

[0005] To sum up, the existing face authentication methods can not take into account the authentication performance and security performance, which restricts the practical application of the algorithm.

Aiming at the problem that the existing face authentication system cannot balance system security and user privacy, a combination of Paillier encryption algorithm and inadvertent transmission is proposed to encrypt the features extracted by the deep neural network, without reducing the system authentication performance. Ensure that users' private information is not leaked, resist malicious attacks, and enhance system security

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