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Optimizing Region of Interest Selection for Effective Embedding in Video Steganography Based on Genetic Algorithms

Nizheen A. Ali1, Ramadhan J. Mstafa2,3,*

1 Department of Computer Science, College of Science, University of Duhok, Duhok, 42001, Iraq
2 Department of Computer Science, Faculty of Science, University of Zakho, Zakho, 42002, Iraq
3 Department of Computer Science, College of Science, Nawroz University, Duhok, 42001, Iraq

* Corresponding Author: Ramadhan J. Mstafa. Email: email

Computer Systems Science and Engineering 2023, 47(2), 1451-1469. https://doi.org/10.32604/csse.2023.039957

Abstract

With the widespread use of the internet, there is an increasing need to ensure the security and privacy of transmitted data. This has led to an intensified focus on the study of video steganography, which is a technique that hides data within a video cover to avoid detection. The effectiveness of any steganography method depends on its ability to embed data without altering the original video’s quality while maintaining high efficiency. This paper proposes a new method to video steganography, which involves utilizing a Genetic Algorithm (GA) for identifying the Region of Interest (ROI) in the cover video. The ROI is the area in the video that is the most suitable for data embedding. The secret data is encrypted using the Advanced Encryption Standard (AES), which is a widely accepted encryption standard, before being embedded into the cover video, utilizing up to 10% of the cover video. This process ensures the security and confidentiality of the embedded data. The performance metrics for assessing the proposed method are the Peak Signal-to-Noise Ratio (PSNR) and the encoding and decoding time. The results show that the proposed method has a high embedding capacity and efficiency, with a PSNR ranging between 64 and 75 dBs, which indicates that the embedded data is almost indistinguishable from the original video. Additionally, the method can encode and decode data quickly, making it efficient for real-time applications.

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APA Style
Ali, N.A., Mstafa, R.J. (2023). Optimizing region of interest selection for effective embedding in video steganography based on genetic algorithms. Computer Systems Science and Engineering, 47(2), 1451-1469. https://doi.org/10.32604/csse.2023.039957
Vancouver Style
Ali NA, Mstafa RJ. Optimizing region of interest selection for effective embedding in video steganography based on genetic algorithms. Comput Syst Sci Eng. 2023;47(2):1451-1469 https://doi.org/10.32604/csse.2023.039957
IEEE Style
N.A. Ali and R.J. Mstafa, "Optimizing Region of Interest Selection for Effective Embedding in Video Steganography Based on Genetic Algorithms," Comput. Syst. Sci. Eng., vol. 47, no. 2, pp. 1451-1469. 2023. https://doi.org/10.32604/csse.2023.039957



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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