Face Security Authentication System Based on Deep Learning and Homomorphic Encryption
Dechao Sun, Hong Huang, Dongsong Zheng, HaoLiang Hu, Chunyue Bi, Renfang Wang
Abstract
The development of deep learning technology has promoted the wide application of face recognition in many scenarios such as mobile payment and social media, but the security of user data is facing great challenges. To protect the privacy of users, face authentication cannot be operated in plaintext. To solve this problem, a face feature ciphertext authentication scheme based on homomorphic encryption is proposed. First, the face image feature extraction is completed based on a deep learning model. Second, the face features are packaged into ciphertext by using homomorphic encryption and batch processing technology, and the face feature ciphertext is saved in the database of the cloud server. Third, combined with automorphism mapping and Hamming distance, a face feature ciphertext recognition method is designed, which can complete face recognition in the case of ciphertext. Finally, the integrity and consistency of face feature ciphertext recognition results before and after decryption are guaranteed by the one-time MAC authentication method. The whole framework can finish identity recognition without decrypting face feature coding, and the homomorphic ciphertext of face feature coding is saved in the database, so there is no risk of face feature coding leakage. Experiments show that the system has met the requirements of real application scenarios.