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Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Optimized image steganography using heterogeneous image embedding techniques

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pp. 2431–2440Vol. 29Issue 6June 2026DOI: 10.47974/JDMSC-2751 Crossmark XML
Received:
01 Nov 2025
Published Online:
20 Jun 2026
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2751
Pages:
2431–2440

Abstract

The need of safe information hiding has been on the rise in the context of the digital communication with the growth of sharing of online data spread exponentially. This paper presents a new method called the optimized Heterogeneous Image Steganography Network (HISN) in which the various attributes of heterogeneous images are used to greatly improve data hiding and security. HISN makes secret images highly invisible and accurate under cover images using convolutional neural networks. HISN, being made of three networks, which are interconnected, the Preparation Network, the Hiding Network and the Reveal Network is highly sensitive to control the optimized embedding processes and the decoding processes. HISN is strong and reliable, with a training accuracy of 92.64% and a testing accuracy of 90.47. The cover images are similar to the original images and the results of the encoder images, the secret images are similar to the original images, so there is an effective data hiding and recovery. 

Keywords

Subject Classifications

Primary 94A08Secondary 54H30

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