I-SteganoGAN: Comprehensive Enhancement of Steganographic Models for Robust Digital Communication Security

This study examines improvements in stegano-graphic models by redesigning three main components: the encoder, decoder, and evaluator, each optimized for better performance. The encoder is designed to embed data effectively with minimal quality, using popular activation function like CeLU. The decoder focuses on accurately retrieving embedded data, essential when images are processed under various conditions. The evaluator uses new functions to accurately check how well data is recovered, promoting continuous improvements. Experimental tests conducted on the Div2K and COCO datasets revealed varied results. The Basic model managed to embed a lot of data (BPP of 4.160), but this reduced image quality significantly, as shown by a PSNR of 30.026 and SSIM of 0.824. In contrast, the Residual model preserved high image quality, with a PSNR of 37.113 and SSIM over 0.9, but embedded less data (BPP of 4.127). The Dense model struck a good balance, embedding a fair amount of data (BPP of 4.512) while maintaining decent image quality (PSNR of 37.089 and SSIM of 0.907). These outcomes demonstrate the potential benefits of making targeted changes to model architecture, suggesting great promise for improving security features in digital communication tools.