Steganography and steganalysis are one of the research hotspots in the field of information security. The abuse of steganography has caused many potential safety hazards. For example, illegal elements use steganography for covert communications to carry out terrorist attacks. The design of traditional steganalysis methods requires a large amount of prior knowledge, and the steganalysis methods based on deep learning use the powerful representation learning ability of the network to autonomously extract abnormal image features, which greatly reduces human participation and achieves good results. To promote the research of steganalysis technology based on deep learning, this study analyzes and summarizes the main methods and work in the field of steganalysis. Firstly, this study analyzes and compares the differences between traditional steganalysis and deep learning-based steganalysis. Furthermore, according to the different training methods, the steganalysis models based on deep learning are divided into two categories: semi-learning steganalysis model and full-learningsteganalysis model. The network structure and detection effect of various types of steganalysis based on deep learning are introduced in detail. In addition, the challenges that the adversarial samples pose to deep learning security are analyzed and summarized, the detection method of adversarial samples is expounded based on steganalysis. Finally, this study summarizes the pros and cons of existing steganalysis models based on deep learning and discusses its development trends.