Deep Learning-Empowered Image Steganography: Architectural Innovations and Performance Benchmarking
Narendra Kumar Chahar, Arvind Dhaka, Amita Nandal, Vijay Kumar · The European Journal on Artificial Intelligence · 2025
In the rapidly evolving field of digital security, this study aims to advance image steganography by developing and benchmarking seven deep learning architectures with a focus on imperceptibility, embedding capacity, and robustness against steganalysis. The models implemented include the residual dense network (RDN), vision transformer with adaptive attention (ViT-AA), progressive generation network (PGN), dual-stream architecture (DSA), wavelet-based hybrid network (WHN), mutual attention transformer (MAT), and efficient attention pyramid transformer (EAPT). Using the PyTorch framework and standardized datasets such as DIV2K, COCO, and ImageNet, each architecture was trained through structured preprocessing and evaluated using metrics including PSNR, SSIM, LPIPS, and statistical steganalysis resistance. Experimental results demonstrate that WHN achieved the highest visual quality (PSNR = 43.5 dB, SSIM = 0.995), while MAT and EAPT provided superior security with detection rates near random chance (0.501–0.502) and robustness against JPEG compression and noise insertion. PGN and DSA offered low-latency performance suitable for resource-constrained or mobile applications, while ViT-AA provided a balanced trade-off across imperceptibility and robustness. The findings confirm that deep learning approaches surpass traditional methods and establish new computational benchmarks for covert communication and digital forensics. These results recommend WHN, MAT, and EAPT for high-security contexts, PGN and DSA for embedded platforms, and ViT-AA as a general-purpose framework, while encouraging further research into lightweight variants for IoT and real-time deployments. Tools for data collection and experimentation included benchmark datasets and PyTorch-based implementations.