Comparison of computational complexity of neural network models for detection of steganographic algorithms
Pages 7 - 11
Abstract
The purpose of the paper is to compare the computational complexity of the steganalytic method created by the authors in previously developed and presented by other academics to detect three steganographic algorithms, including jUniward, nsF5 and UERD, against other solutions found during the literature survey. The article describes shortly each analyzed steganographic algorithm along with all compared solutions, gives their characteristic points, and includes list of the model specifications. Moreover, the metric comparing the computational complexity, so the number of parameters in a given neural network, is presented. Since none of the compared articles provided this parameter, there is provided algorithm by which these values could be calculated for each analyzed model. The comparison of the results and final conclusions are presented to explain the distinctions between the obtained outcomes and to identify the factors which could have affected such performance.
References
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Mikołaj Płachta and Artur Janicki. 2022. A Simple Neural Network for Detection of Various Image Steganography Methods. Proceedings of the 3rd International Conference on Data Science and Machine Learning - DSML 2022, Computer Science & Information Technology 12, 15 (2022). https://doi.org/
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Index Terms
- Comparison of computational complexity of neural network models for detection of steganographic algorithms
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Published In
February 2024
395 pages
ISBN:9798400708329
DOI:10.1145/3651781
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 30 May 2024
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ICSCA 2024
ICSCA 2024: 2024 13th International Conference on Software and Computer Applications
February 1 - 3, 2024
Bali Island, Indonesia
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