Abstract: In this paper, we present a new hybrid image steganography algorithm by combining two famous techniques which are curvelet transform and genetic algorithm GA. The proposed algorithm is called Hybrid Curvelet Transform and Genetic Algorithm for image steganography (HCTGA). Curvelet transform is a multiscale geometric analysis tool, its main advantage is that it can solve the important problems efficiently and other transforms are not that ideal. Genetic algorithm is a famous optimization algorithm with the aim of finding the best solutions to a given computational problem that maximizes or minimizes a particular function. In the proposed algorithm the cover and secret images are passed through a preprocessing process by applying four different filters to them in order to remove the noise and achieve a better quality to both images before the hiding process. Then the curvelet transform is applied to the cover image to find the curvelet frequencies of the image, and the secret image is hided at the Least Significant Bits (LSB) of the curvelet frequencies of the cover image to reconstruct the stego image. Finally genetic algorithm operations are employed to find different scenarios for the hiding process by rearranging the hiding bits and finally choose the best scenario that can reach a better image quality and a higher Peak Signal to Noise Ratio (PSNR) results.

Heba Mostafa Mohamed, Ahmed Fouad Ali and Ghada Sami Altaweel, “A Hybrid Curvelet Transform and Genetic Algorithm for Image Steganography” International Journal of Advanced Computer Science and Applications(IJACSA), 8(8), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080843

@article{Mohamed2017,
title = {A Hybrid Curvelet Transform and Genetic Algorithm for Image Steganography},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080843},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080843},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {8},
author = {Heba Mostafa Mohamed and Ahmed Fouad Ali and Ghada Sami Altaweel}
}