TY  - JOUR
AU  - Shaik Razia
AU  - Sk Khamer Taj
AU  - S Indupriya
AU  - S Md Ismail
AU  - M N Dattatreya
AU  - K Babji
PY  - 2026
DA  - 2026/04/25
TI  - Image Steganography With CNN-Based Encoder– Decoder Model
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 6
IS  - 4
AB  - 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
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1291
DO  - 10.33425/3066-1226.1291