As an important carrier of information transmission, images are widely used in human-computer interaction, digital twins, cloud computing, and even people’s daily life. Image security is directly related to personal privacy, business secrets and national security. It is of great significance to strengthen the research of image security. Deep learning is a machine learning method based on neural networks, which enables the processing and analysis of complex data by simulating the way human brain neurons work and provides strong support for research in the field of image security. With the rapid development of deep learning technology, its application in the field of image processing and security is increasingly extensive. To gain a deeper understanding of the current situation and cutting-edge trends of deep learning-based image security research. This paper uses CiteSpace, a bibliometric tool, to analyze the 2009-2022 research results on "image security" and "deep learning" in the core database of Web of Science. Accurately grasp the stage of development and characteristics of image security research in the world through data analysis. It is found that the research hotspots mainly cover image encryption, image steganography, image watermarking, and other technical applications. In the past decade, great progress has been made in the field of image security. The research on techniques such as synthetic image identification, image tampering detection, and adversarial samples is rapidly emerging. Future research on image security deep learning-based will focus on the directions of anti-attacks, robustness enhancement, privacy protection, and data sharing. We hope this study will provide new research ideas and thinking perspectives to draw on for the future direction of image security.