CNN-Driven Deep Learning-Based Image Steganorgraphy Utilizing Multiple Image Processing Operation

In this work, we present a new CNN-based steganographic technique that can safely embed many picture layouts into a single host image. Our method efficiently hides different picture formats under a cover image by using convolutional neural networks, in contrast to previous algorithms that usually concentrate on hiding details into a single format. The deep learning model is optimized to disturb the cover image as little as possible to make it look invisible and unnoticeable to hide itself. This one of the responsive embedding ways various data types of further aid to enhance flexibility and robustness of the procedure. We demonstrate using our findings that this CNN-centric approach is less detectable by modern detection approaches in comparison to general steganography systems delivering better hiding capacity and visual quality.