Advancements in adversarial example defense for deep learning models: a review

Abstract Artificial intelligence technology based on deep learning has been widely used in key fields such as automatic driving, medical diagnosis and financial risk control. These applications also bring more and more serious security problems. In particular, as the means of attack continue to evolve, well-designed countermeasures seriously threaten the reliability of the model and the security of the system. In order to deal with this risk, defensive confrontation samples have become the core task of AI security research, playing a key role in improving the security and credibility of the model. Aiming at the problems of unclear concepts and overlapping standards in previous classification methods, this paper proposes a clearer and unified classification framework, combs and defines the contents of existing research, and solves the inconsistencies. The framework systematically divides the existing countermeasures and defense methods into three categories: detection, purification and optimization. This classification will help researchers understand the actual effects of different methods in the face of various attacks more clearly. This paper also analyzes the tradeoffs between accuracy, robustness, operational efficiency and generalization capability of various defense mechanisms, and reveals how they balance the calculation cost and actual deployment requirements. In addition, the paper points out the main challenges facing the current research, and puts forward the future research directions, including developing more efficient, adaptive, and cross modal defense methods to comprehensively improve the security of AI systems. The purpose of this review is to help researchers understand the development process of anti sample defense technology and provide a reference path for building a stable and reliable AI system.