Hybrid image steganography method using Lempel Ziv Welch and genetic algorithms for hiding confidential data - Almawgani, Abdulkarem H. M.; Alhawari, Adam R. H.; Hindi, Ayman Taher; Al-Arashi, Waled Hussein; Al-Ashwal, A. Y. (Springer Nature, 2022)
Digital images are commonly used in steganography due to the popularity of digital image transfer and exchange through the Internet. However, the tradeoff between managing high capacity of secret data and ensuring high security and quality of stego image is a major challenge. In this paper, a hybrid steganography method based on Haar Discrete Wavelet Transform, Lempel Ziv Welch algorithm, Genetic Algorithm, and the Optimal Pixel Adjustment Process is proposed to improve the quality of the stego image and increase the capacity for secret data. The cover image is divided into non-overlapping blocks of n n pixels. Then, the Haar Discrete Wavelet Transform is used to increase the robustness of the stego image against attacks. In order to increase the capacity for, and security of, the hidden image, the Lempel Ziv Welch algorithm is applied on the secret message. After that, the Genetic Algorithm is employed to give the encoded and compressed secret message cover image coefficients. The Genetic Algorithm is used to find the optimal mapping function for each block in the image. Lastly, the Optimal Pixel Adjustment Process is applied to reduce the error, i.e., the difference between the cover image blocks and the stego image blocks. This step is a further improvement to the stego image quality. The proposed method was evaluated using four standard images as covers and three types of secret messages. The results demonstrate higher visual quality of the stego image with a large size of embedded secret data than what is generated by already-known techniques. The experimental results show that the information-hiding capacity of the proposed method reached to 50% with high PSNR (52.83 dB). Thus, the herein proposed hybrid image steganography method improves the quality of the stego image over those of the state-of-the-art methods.