A Novel Approach to Image Synthesis: Using Stack GAN to Enhance Cybersecurity Application of Generative AI

Text-to-image synthesis, a transformative application of artificial intelligence, converts textual descriptions into vivid images, with implications ranging from artistic expression to cybersecurity. This paper explores Stack Generative Adversarial Networks (Stack GAN) as an architectural innovation, enhancing image quality in two stages: Stage I and Stage II, each employing generators and discriminators to produce high-resolution images. With a focus on data integrity and system security, encryption algorithms safeguard sensitive input data. The Stack GAN model, coupled with conditional augmentation, improves resource efficiency, and content linkage, and mitigates over-fitting. Results demonstrate the model's robustness, offering utility in information and network security through enhanced fraud detection, user authentication, and visualization of security policies. This research underscores the fusion of AI's creative capabilities with traditional and innovative security measures.