Adversarial phenomena have been widely observed in machine learning (ML) systems, especially those using deep neural networks. These phenomena describe situations where ML systems may produce predictions that are inconsistent and incomprehensible to humans in certain specific cases. Such behavior poses a serious security threat to the practical application of ML systems. To exploit this vulnerability, several advanced attack paradigms have been developed, mainly including backdoor attacks, weight attacks, and adversarial examples. For each individual attack paradigm, various defense mechanisms have been proposed to enhance the robustness of models against the corresponding attacks. However, due to the independence and diversity of these defense paradigms, it is challenging to assess the overall robustness of an ML system against different attack paradigms. This survey aims to provide a systematic review of all existing defense paradigms from a unified lifecycle perspective. Specifically, we decompose a complete ML system into five stages: pre-training, training, post-training, deployment, and inference. We then present a clear taxonomy to categorize representative defense methods at each stage. The unified perspective and taxonomy not only help us analyze defense mechanisms but also enable us to understand the connections and differences among different defense paradigms. It inspires future research to develop more advanced and comprehensive defense strategies.
Wu et al. (2025) studied this question.