STUDYING THE RESISTANCE OF BIOMETRIC AUTHENTICATION SYSTEMS TO ATTACKS USING VOICE CLONING TECHNOLOGY BASED ON DEEP NEURAL NETWORKS

With the development of voice synthesis technologies based on deep neural networks, the security threats to biometric authentication systems that use voice recognition have increased. These systems, which were considered reliable, can be easily compromised by fake voices created using advanced models such as WaveNet, Tacotron 2, and other modern algorithms. In today's cybersecurity environment, such attacks jeopardize the confidentiality of personal data, which necessitates the improvement of protection methods. The purpose of this article is to study the resilience of biometric authentication systems to attacks using voice cloning technology, to analyze the effectiveness of modern synthesis methods for circumventing such systems, and to provide a comparative overview of various approaches to protect voice biometric data. The article discusses technologies that allow for the creation of accurate and realistic synthetic voices, as well as methods for detecting and protecting against fake signals. The article also analyzes the current vulnerabilities of voice systems and suggests strategies to increase resistance to such attacks, providing users with greater security and privacy.