A Survey of Adversarial Examples in Computer Vision: Attack, Defense, and Beyond

Recent years have witnessed the ever-increasing performance of Deep Neural Networks (DNNs) in computer vision tasks. However, researchers have identified a potential vulnerability: carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data. In this survey, we focus on (1) adversarial attack algorithms to generate adversarial examples, (2) adversarial defense techniques to secure DNNs against adversarial examples, and (3) important problems in the realm of adversarial examples beyond attack and defense, including the theoretical explanations, trade-off issues and benign attacks in adversarial examples. Additionally, we draw a brief comparison between recently published surveys on adversarial examples, and identify the future directions for the research of adversarial examples, such as the generalization of methods and the understanding of transferability, that might be solutions to the open problems in this field.