As an important technology in the field of artificial intelligence (AI), deep neural networks are widely used in various image classification tasks. However, existing studies have shown that deep neural networks have security vulnerabilities and are vulnerable to adversarial examples. At present, there is no research on the systematic analysis of adversarial example detection of images. To improve the security of deep neural networks, this study, based on the existing research work, comprehensively introduces adversarial example detection methods in the field of image classification. First, the detection methods are divided into supervised detection and unsupervised detection by the construction method of the detector, which are then classified into subclasses according to detection principles. Finally, the study summarizes the problems in adversarial example detection and provides suggestions and an outlook in terms of generalization and lightweight, aiming to assist in AI security research.