Security Issues and Defensive Approaches in Deep Learning Frameworks

Abstract

Abstract: Deep learning frameworks are instrumental in advancing artificial intelligence, showcasing vast potential across diverse applications. Despite their transformative impact, security concerns pose significant risks, impeding widespread adoption. Malicious internal or external attacks on these frameworks can have far-reaching consequences for society. Our research delves into the intricacies of deep learning algorithms, conducting a thorough analysis of vulnerabilities and potential attacks. To address these challenges, we propose a comprehensive classification system for security issues and corresponding defensive approaches within deep learning frameworks. By establishing clear connections between specific attacks and their defenses, we aim to enhance the robustness of these frameworks. In addition to theoretical considerations, our study extends to real-world applications, exploring a case involving the practical implications of deep learning security issues. Looking ahead, we discuss future directions and open issues within the realm of deep learning frameworks, aspiring to inspire further developments. We anticipate that our research will not only contribute valuable insights but also attract attention from both academic and industrial domains, fostering a collective commitment to fortifying the security of deep learning frameworks.