A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies. (November 2022)

• Record Type: Journal Article
• Authors: Qian, Zhuang; Huang, Kaizhu; Wang, Qiu-Feng; Zhang, Xu-Yao
• Is Part Of: Pattern recognition. Volume 131(2022)
• Publication Date: 2022-11
• Subjects: Adversarial examples -- Adversarial training -- Robust learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108889
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We present a timely and comprehensive survey on robust adversarial training. This survey offers the fundamentals of adversarial training, a unified theory that can be used to interpret various methods, and a comprehensive summarization of different methodologies. This survey also addresses three important research focus in adversarial training: interpretability, robust generalization, and robustness evaluation, which can stimulate future inspirations as well as research outlook. Abstract: Deep neural networks have achieved remarkable success in machine learning, computer vision, and pattern recognition in the last few decades. Recent studies, however, show that neural networks (both shallow and deep) may be easily fooled by certain imperceptibly perturbed input samples called adversarial examples. Such security vulnerability has resulted in a large body of research in recent years because real-world threats could be introduced due to the vast applications of neural networks. To address the robustness issue to adversarial examples particularly in pattern recognition, robust adversarial training has become one mainstream. Various ideas, methods, and applications have boomed in the field. Yet, a deep understanding of adversarial training including characteristics, interpretations, theories, and connections among different models has remained elusive. This paper presents a comprehensive survey trying to offer a systematic and structured investigation on robust adversarial training in pattern recognition. We start with fundamentals including definition, notations, and properties of adversarial examples. We then introduce a general theoretical framework with gradient regularization for defending against adversarial samples - robust adversarial training with visualizations and interpretations on why adversarial training can lead to model robustness. Connections will also be established between adversarial training and other traditional learning theories. After that, we summarize, review, and discuss various methodologies with defense/training algorithms in a structured way. Finally, we present analysis, outlook, and remarks on adversarial training.