Image Steganography With CNN-Based Encoder– Decoder Model
Shaik Razia,
Sk Khamer Taj,
S Indupriya,
S Md Ismail,
M N Dattatreya,
K Babji
Image steganography is a critical domain in information security that focuses on concealing secret data
within digital images to ensure secure and covert communication. Traditional steganographic techniques,
such as Least Significant Bit (LSB) substitution and transform-domain methods, often suffer from limited
payload capacity, low robustness, and vulnerability to detection. To overcome these limitations, this
paper proposes a deep learning-based image steganography framework using a Convolutional Neural
Network (CNN) integrated with an encoder–decoder architecture for efficient and secure data hiding.
The proposed model consists of two main components: an encoder network and a decoder network. The
model maximizes the quality of the recovered secret image. Experimental results demonstrate that the
proposed approach significantly improves data hiding capacity and ensuring reliable reconstruction of
hidden data. The model
provides a scalable and efficient solution for secure communication, making it suitable for applications
such as digital watermarking, and covert information transfer
