New steganography algorithm to conceal a large amount of secret message using hybrid adaptive neural networks with modified adaptive genetic algorithm

A New Image Steganography Algorithm Based on MLSB Method with Random Pixels Selection

“…The results confirm obviously that the proposed method is more secure and preserved secret information than the other steganographic schemes. It appears that the average of three stego images in the proposed approach is better than (EL-EMAM, N. 2013) [14] and (Chang, C., 2008) [28] about 11.23% and 14.42% respectively. In Table 4, the performance of the proposed algorithm has been checked using five measures; these measures have been discussed through the PSNR, see Eq.…”

“…Propose a method using two secret images to hide into one cover image to produce a high quality of a stg. However, the quality of Stg produced in this approach was not promising due to a large payload capacity (Hong, W., et al, 2010) El-Emam, N., Al-Zubidy, R., (2013) [14] proposed steganography algorithm to hide a large amount of secret messages into a cover image by using four security layers. Moreover, this algorithm presents image segmentation algorithm and intelligent technique based on adaptive neural networks with genetic algorithm.…”

Data Hiding Schemes: A Survey

“…In the last two decades, artificial intelligence (AI) techniques such as evolutionary algorithms (EAs), support vector machine, fuzzy logic and neural networks have played an important role in data hiding [25,71,72,83,95,94,99,105,116,121] for improving the performance. Under the category of evolutionary algorithms, genetic algorithm (GA) [7,23,25,47,71,72,81], particle swarm optimization (PSO) [53,85,94,99,108], differential evolution (DE) [4,6,2,8,52], Firefly algorithm [76], and artificial bee colony (ABC) [5] have made numerous valuable contributions to the field of data hiding. An image watermarking technique which uses a GA to find the optimal scaling factors for watermark insertion is designed by Lai [47].…”

“…The performance of data hiding schemes also improved by the hybridization of these artificial intelligence techniques [25,71,72,94]. Tsai et al [94] proposed a zerowatermark (lossless) scheme with geometrical invariants using support vector machine (SVM) classifier against geometrical attacks for image authentication.…”

“…And the nearly optimal parameters of the SVM are obtained by particle swarm optimization (PSO) algorithm. The hybridization of genetic algorithm and neural networks for the data hiding schemes can be found in [25,72]. Maity et al [71] proposed a watermarking scheme based on GA and fuzzy hybridization.…”