Title: Magnitude-based nonlinear steganography approach with image using assisted prediction from artificial neural networks

Authors: Sabyasachi Samanta; Sudipta Roy; Dipak Kumar Jana

Addresses: Department of Computer Science and Engineering (Cyber Security), Haldia Institute of Technology, Haldia, Purba Midnapur – 721657, West Bengal, India ' Department of Computer Science and Engineering (Cyber Security), Haldia Institute of Technology, Haldia, Purba Midnapur – 721657, West Bengal, India ' Gangarampur College, Gangarampur, Dakshin Dinajpur, West Bengal – 733124, India

Abstract: A unique magnitude-based nonlinear image steganography technique is presented in this study, tackling the crucial trade-off between payload capacity and imperceptibility. Our method, in contrast to conventional methods, uses a secret key to start a pseudo-random pixel selection procedure that is dynamically directed by the payload. In order to maximise capacity and control distortion, this payload-adaptive technique produces a complicated, unpredictable embedding pattern. Crucially, information is only extracted from these specific pixel/bit positions, increasing efficiency and reducing vulnerability to steganalysis based on local statistical abnormalities. Through the prediction of the payload from the stego-image, we use an artificial neural network (ANN) to verify embedding accuracy. A thorough statistical analysis that contrasts our approach with existing methods shows that it performs better in terms of payload capacity, imperceptibility (PSNR/SSIM), and resistance to steganalysis. This special steganographic framework, which advances the state-of-the-art and provides a promising path for secure communication, is established by the combination of magnitude-based nonlinear embedding, payload-driven pixel selection, secret key initialisation, and ANN-based validation.

Keywords: information security; payload; bits per pixel; BPP; magnitude-based nonlinear pixel position; MNPP; steganography; artificial neural network; ANN; statistical measure.

DOI: 10.1504/IJICS.2025.149447

International Journal of Information and Computer Security, 2025 Vol.28 No.3, pp.304 - 326

Received: 04 Mar 2024
Accepted: 10 Mar 2025
Published online: 31 Oct 2025 *

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