High-Quality Synthetic Image Generation Using Deep Learning Technique: Application to Healthcare Sector

Generative Adversarial Networks (GANs) have significantly transformed the field of image generation, enabling the creation of highly realistic and diversified images. However, as these capabilities continue to advance, there are growing concerns surrounding privacy. This review paper delves into the multifaceted landscape of privacy preservation within the realm of GAN-based image generation. We explore the broad applications of GANs across healthcare, and more, emphasizing their growing relevance in everyday life. Concurrently, we address the pressing concerns related to privacy, emphasizing the potential misuse of GANs for deep fake creation, identity theft, and other malicious activities. To mitigate these issues, we discuss various techniques and strategies embedded within GAN models aimed at preserving privacy. These techniques include differential privacy and novel architectural designs that prioritize data protection while maintaining the rich generative capacity of GANs. This review provides a comprehensive overview of the intricate balance between leveraging the power of GANs and safeguarding individual privacy, offering valuable insights for researchers, practitioners, and policymakers working in this dynamic field.