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Exponential fractional cat swarm optimization for video steganography

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Abstract

In this paper, an effective method named Exponential Fractional-Cat Swarm Optimization (Exponential Fractional-CSO) along with multi-objective cost function is proposed. The proposed method is designed by integrating the CSO with the fractional concept based on the Exponential parameters. Initially, an input video is selected from the database from which frames are generated. Key frames are chosen among the frames using the contourlet transform and Structural Similarity Index Measure (SSIM). Regions are formed on the selected key frames through the help of grid lines. Once the regions are formed, optimal regions are ascertained with the help of the proposed optimization algorithm along with multi-objective cost functions to hide the secret data. During the embedding process, the secret data is hidden in the optimal region using the lifting wavelet transform (LWT). The embedded video is then transmitted through the network to reach its intended receiver. The experimental results reveal that the proposed Exponential Fractional-CSO obtained a maximal correlation of 0.9931 by considering the frames, maximal Peak Signal-to-Noise Ratio (PSNR) of 89.70 dB and MSE of 0.00006 respectively. Hence, the proposed method shows greater effectiveness of hiding the secret data in the video sequence along with data security.

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Authors and Affiliations

  1. Deparment of Computer Science, Nesamony Memorial Christian College, affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    Meenu Suresh

  2. Department of PG Computer Science, Nesamony Memorial Christian College, affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    I. Shatheesh Sam

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  1. Meenu Suresh
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Correspondence to Meenu Suresh.

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Suresh, M., Sam, I.S. Exponential fractional cat swarm optimization for video steganography. Multimed Tools Appl 80, 13253–13270 (2021). https://doi.org/10.1007/s11042-020-10395-6

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