SAFEGUARDING PYTORCH MODELS: STRATEGIES FOR SECURING DEEP LEARNING PIPELINES

Abstract

As machine learning models become increasingly prevalent in critical applications across various industries, the imperative to secure these models against a wide array of potential threats and vulnerabilities has never been more pressing. This paper presents a comprehensive examination of security considerations for models built using PyTorch, one of the most popular deep learning frameworks in use today. We conduct an in-depth analysis of potential attack vectors that PyTorch models may face, including model theft, data extraction, adversarial attacks, and supply chain vulnerabilities. The paper then delves into the built-in security features offered by PyTorch, such as secure model loading options and input validation recommendations, evaluating their effectiveness and limitations. Building on this foundation, we propose and discuss a set of best practices for hardening PyTorch models against threats, encompassing secure model loading techniques, rigorous input sanitization methods, isolated execution environments, and the implementation of encryption and privacy-preserving techniques. The research also explores emerging security paradigms in the field, including federated learning, differential privacy, and homomorphic encryption, assessing their potential applications in enhancing PyTorch model security. Through case studies and code examples, we demonstrate practical implementations of these security measures. Finally, the paper concludes with a forward-looking discussion on the evolving landscape of AI security, emphasizing the need for continued vigilance and adaptation of security practices to address new and emerging threats in the rapidly advancing field of machine learning.