A novel block based preparation network using convolutional neural networks for image steganography and robust data protection

Apr 1, 2026·
Aditya Dabhade
,
Soumendu Chakraborty
Dr. Snigdha Sen
Dr. Snigdha Sen
· 0 min read
DOI
Abstract
Image steganography preserves privacy by embedding hidden information within a cover image. This research presents a streamlined Convolutional Neural Network (CNN) model that embeds a concealed image inside a cover image and retrieves it using an encoder-decoder design. Unlike conventional least-significant-bit techniques, it spreads and compresses the hidden image’s information across all available bits. Evaluated with SSIM and PSNR, the method reports PSNR 44.37 and SSIM 0.9956, outperforming existing deep-learning steganographic methods with better imperceptibility, security, robustness and hiding capacity.
Type
Publication
Multimedia Tools and Applications
publications
Deep Learning Image Steganography
Dr. Snigdha Sen
Authors
Dr. Snigdha Sen
Senior Assistant Professor, School of Computer Engineering
Dr. Snigdha Sen is a Senior Assistant Professor in the School of Computer Engineering at Manipal Institute of Technology Bengaluru, MAHE, with around 16 years of teaching experience. Before joining Manipal she was an Associate Professor at Global Academy of Technology, Bengaluru. She holds a PhD from IIIT Allahabad, an M.Tech in Information Technology from Jadavpur University and a B.E. from UIT, University of Burdwan. Her research spans astroinformatics, deep learning, computer vision, explainable AI for space science and big data analytics. She has published over 40 SCI- and Scopus-indexed papers and is a life member of the Computer Society of India.