Exploration of Image Privacy Protection Technology Using Generative Adversarial Networks

As digital images spread widely, privacy protection has become a critical issue. This study focuses on exploring the application of Generative Adversarial Networks (GANs) in image privacy protection. By deeply analyzing the principles of GANs, we innovatively introduce differential privacy technology to construct a DP-GANs model. Experimental results using public image datasets show that this model can generate realistic substitute images, effectively protecting privacy. Not only does it demonstrate potential applications in fields such as healthcare and security, but it also significantly enhances image usability, injecting new momentum into the development of privacy protection technologies and facilitating the secure circulation of image data