Review of Artificial Intelligence Adversarial Attack and Defense Technologies

TLDR

Artificial intelligence is increasingly deployed across computer vision, NLP, autonomous driving, and other domains, yet its vulnerability to adversarial attacks hampers its use in security‑critical applications. This review synthesizes recent advances in deep‑learning adversarial attack and defense research. The authors organize attacks by training and testing stages, survey their applications in vision, language, cyberspace, and physical settings, and classify defenses into data‑based, model‑based, and auxiliary‑tool approaches.

Abstract

In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools.