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Image steganalysis with entropy hybridized with chaotic grasshopper optimizer

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Abstract

We propose a hybrid grasshopper optimizer to reduce the size of the feature set in the steganalysis process using information theory and other stochastic optimization techniques. This paper results from the stagnancy of local minima and slow convergence rate by the grasshopper algorithm in optimization problems. Therefore, we enhance the grasshopper optimization (GOA) performance with chaotic maps to make it Chaotic GOA (CGOA). Then, we combine the CGOA with adaptive particle swarm optimization (APSO) to make it Chaotic Particle-Swarm Grasshopper Optimization Algorithm (CPGOA). Next, we use the proposed optimizer with entropy to find the best feature subset of the original Subtractive Pixel Adjacency Model (SPAM) and Spatial Rich Model (SRM) feature set. Finally, the proposed technique is experimented with to detect the spatial domain steganography with different embedding rates on the BOSSbase 1.01 grayscale image database. The results show the improved results from the proposed hybrid optimizer compared to the original GOA and other state-of-the-art feature selection methods in steganalysis.

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Acknowledgments

One of the authors, Rajeev Kumar thanks the reviewers for their valuable comments, by which the understanding and readability of this manuscript are greatly improved. He also thanks a member of his research group, Priti Kumari, for drawing a few figures. Finally, he is thankful to the Editor-in-Chief, the Editor, and the Editorial Office Assistant(s) for managing this manuscript.

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  1. School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, 110 067, India

    Sonam Chhikara & Rajeev Kumar

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  1. Sonam Chhikara
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Correspondence to Rajeev Kumar.

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Sonam Chhikara Deceased on Dec. 16, 2019. This article is dedicated to her memory.

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Chhikara, S., Kumar, R. Image steganalysis with entropy hybridized with chaotic grasshopper optimizer. Multimed Tools Appl 80, 31865–31885 (2021). https://doi.org/10.1007/s11042-021-11118-1

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