Abstract
A deep neural network is used to develop a covert communication and textual data extraction strategy based on steganography and picture compression in such work. The original input textual image and cover image are both pre-processed before the covert text-based pictures are separated and implanted into the least significant bit of the cover object picture element using spatial steganography. Following that, stego-images are compressed and transformed(by using Leh Transformation) to provide a higher-quality image while also saving storage space at the sender's end. After then, the stego-image will be transmitted to the receiver over a communication link. At the receiver's end, steganography and compression are then reversed. This work contains a plethora of issues, making it an intriguing subject to pursue. The most crucial component of this task is choosing the right steganography and picture compression technology. The proposed technology, which combines picture steganography with compression and transformation, delivers higher peak signal-to-noise efficiency.
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