Enhancing Stego Image Quality With SIUN Post-Processing of Image Steganography Without Embedding DCGAN Outputs

In digital steganography, hiding information seamlessly within images is key. This study merges Deep Convolutional Generative Adversarial Networks (DCGAN) with Scale-Iterative Upscaling Networks (SIUN) to craft high-quality stego images swiftly and enhance the DCGAN image training period. Eschewing length DCGAN training, SIUN refines post-generation images, ensuring detailed visuals and increased data storage. Using the MNIST dataset, findings show that SIUN not only accelerates the process but also improves the stego image quality, suggesting a significant leap forward for secure communication efficiency. This research found that by using SIUN can enhance the quality of stego images with just 50 epochs of DCGAN training. After this initial training, the images are sent to SIUN for further quality upgrades with more efficient time.