Adversarial Attacks and Defenses in Image Classification: A Practical Perspective

The rapid and steady development of machine learning, especially deep learning, has promoted significant progress in the field of image classification. However, Machine learning models are demonstrated to be vulnerable to adversarial examples, which pose serious threats in security-critical applications. This paper summarizes the adversarial attacks and defenses from a practical perspective, facing the field of image classification. We further analyze and evaluate the characteristics and performance of various defense techniques from four aspects: gradient masking, adversarial training, adversarial examples detection and input transformations. We discuss the advantages and disadvantages of different defenses. Finally, the future development trend of defense techniques against adversarial examples is discussed. We hope our study will advance the research in machine learning security.