Abstract: With the rapid development of deep learning, signal modulation recognition based on deep neural networks has gained popularity in wireless communications research. However, it has been observed that the deep neural network model is vulnerable to adversarial perturbations, rendering the modulation identification task ineffective. Currently, there are theoretical gaps and bottlenecks in wireless communication security research. Due to the multidimensional nature of wireless communication, including factors such as experimental environments, data structures, and signal characteristics, it is not feasible to transfer the established attack and defense methods from other domains to signal countermeasures. In this paper, we comprehensively summarize the research on adversarial attack and defense technology in the field of signal modulation recognition. As the first Chinese review of its kind, we propose a generic classification framework and threat model for adversarial attacks in this field. Classify the research in this field into two categories: physical self-defense attacks and digital direct access attacks. Then, systematically integrate and visualize the research as two-dimensional diagrams to demonstratively showcase the methods, models, and techniques of adversarial attack. Additionally, provide details on the methods and models of adversarial attack. We present the latest research on adversarial attack methods, adversarial examples generation techniques, theoretical formulas, and adversarial detection and defense techniques. We systematically refine the characteristics of the three dimensions of adversarial attacks on wireless communications and summarize the corresponding processing methods. Finally, we summarize the future research and development direction of the attack and defense security field oriented towards signal modulation recognition.