title: ÐÐ°ÑÐ¸ÑÐ° Ð¾Ñ Ð·Ð°ÐºÐ»Ð°Ð´Ð¾Ðº Ð² Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÑÑ , Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÐ¼ÑÑ Ð² ÑÐ¸ÑÑÐµÐ¼Ð°Ñ Ð±Ð¸Ð¾Ð¼ÐµÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð°ÑÑÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¿Ð¾ Ð³ÐµÐ¾Ð¼ÐµÑÑÐ¸Ð¸ Ð»Ð¸ÑÐ°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Protection against backdoors in neural networks used in facial geometry biometric authentication systems
Creators: name: ÐÑÐ·ÑÐ¼Ð¸Ð½Ð°, ÐÑÐ¸ÑÑÐ¸Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð·Ð°ÐºÐ»Ð°Ð´ÐºÐ¸
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð±Ð¸Ð¾Ð¼ÐµÑÑÐ¸ÑÐµÑÐºÐ°Ñ Ð°ÑÑÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¼Ð¾Ð´ÐµÐ»Ñ Ð°Ð½ÑÐ°Ð¼Ð±Ð»Ñ
backdoors
neural networks
biometric authentication
ensemble model

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description: The purpose of the study is to protect against backdoors in neural networks used in facial geometry biometric authentication systems. The subjects of the work are detection and neutralization methods for backdoors in neural networks. The research addresses the following goals: 1. Analysis of facial geometry biometric authentication methods. 2. Investigation of approaches to implementing backdoors in neural networks used in biometric authentication systems. 3. Analysis of approaches for detecting and neutralizing backdoors in neural networks. 4. Development of an approach for protecting against backdoors in neural networks used in facial geometry biometric authentication systems, based on an ensemble of neural networks. 5. Creation of a software prototype for a facial geometry biometric authentication system with an integrated approach to backdoor protection. 6. Evaluation of the accuracy and level of protection against backdoors in neural networks in the developed prototype. During the work, modern methods of facial geometry biometric authentication were analyzed. Methods for implementing, detecting, and neutralizing backdoors in neural networks were investigated. As a result, an innovative approach to protecting against backdoors in neural networks used in biometric authentication systems was developed, and the accuracy of the approach was demonstrated. It was concluded that the level of protection of neural networks from backdoor implementation is high. The results obtained can be used as a basis for designing facial geometry biometric authentication systems that are resilient to backdoor implementation.