Deep learning-based image steganography has shown remarkable progress in seamlessly embedding secret information within cover images. However, the opaque nature of neural networks limits our understanding of how and where the model hides and recovers hidden content. In this paper, we introduce an explainability-driven study for interpreting and evaluation of critical region robustness of convolutional encoder-decoder architectures used in image steganography. By leveraging techniques such as Integrated Gradients, SmoothGrad and LRP, we visualize the spatial critical regions and features most responsible for decoding information. We design our experiments around decoder part which studies effects of distortions by applying the different noises to the most salience part of the steganographic image. Our analysis reveals consistent patterns of information flow and identifies key regions that influence the success or failure of steganographic recovery. These insights not only enhance model transparency but also inform future improvements in robustness, and adversarial resistance. This work provides a step towards more interpretable and trustworthy deep steganography systems.