Steganography Genetic Algorithm Hyperparameter Tuning through Response Surface Methodology

Authors

  • Warley Gramacho da Silva Universidade Federal do Tocantins
  • Rafael Lima de Carvalho Universidade Federal do Tocantins
  • Glêndara Aparecida de Souza Martins Universidade Federal do Tocantins

DOI:

https://doi.org/10.20873/uft.2675-3588.2020v1n1p13

Keywords:

RSM, Steganography, Genetic Algorithms, Hyperparameter Optimization

Abstract

Steganography consists of hidding bits of an information source into a host source. In image processing, a common way of doing the hiding process is to break each byte from the message information and embbed into the message bytes in a way that the differences among the original host and the embedded one is minimized. A genetic algorithm can be used to find the proper combination of bits in order to minimize such differences, but some hyperparameters need to be optimized in order to get an optimized performance. This work investigates the application of Response Surface Methodology in order to find the best hyperparameters of a genetic algorithm applied to image steganography.

Published

2020-03-05

How to Cite

[1]
Gramacho da Silva, W. et al. 2020. Steganography Genetic Algorithm Hyperparameter Tuning through Response Surface Methodology. Academic Journal on Computing, Engineering and Applied Mathematics. 1, 1 (Mar. 2020), 13–17. DOI:https://doi.org/10.20873/uft.2675-3588.2020v1n1p13.

Issue

Section

Research Papers

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