Improved Deep Hiding/Extraction Algorithm to Enhance the Payload Capacity and Security Level of Hidden Information

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Marwa Ahmad
Nameer N. EL-Emam
Ali F. AL-Azawi


Steganography algorithms have become a significant technique for preventing illegal users from obtaining secret data. In this paper, a deep hiding/extraction algorithm has been improved (IDHEA) to hide a secret message in colour images. The proposed algorithm has been applied to enhance the payload capacity and reduce the time complexity. Modified LSB (MLSB) is based on disseminating secret data randomly on a cover-image and has been proposed to replace a number of bits per byte (Nbpb), up to 4 bits, to increase payload capacity and make it difficult to access the hiding data. The number of levels of the IDHEA algorithm has been specified randomly; each level uses a colour image, and from one level to the next, the image size is expanded, where this algorithm starts with a small size of a cover-image and increases the size of the image gradually or suddenly at the next level, according to an enlargement ratio. Lossless image compression based on the run-length encoding algorithm and Gzip has been applied to enable the size of the data that is hiding at the next level, and data encryption using the Advanced Encryption Standard algorithm (AES) has been introduced at each level to enhance the security level. Thus, the effectiveness of the proposed IDHEA algorithm has been measured at the last level, and the performance of the proposed hiding algorithm has been checked by many statistical and visual measures in terms of the embedding capacity and imperceptibility. Comparisons between the proposed approach and previous work have been implemented; it appears that the intended approach is better than the previously modified LSB algorithms, and it works against visual and statistical attacks with excellent performance achieved by using the detection error (PE). Furthermore, the results confirmed that the stego-image with high imperceptibility has reached even a payload capacity that is large and replaces twelve bits per pixel (12-bpp). Moreover, testing is confirmed in that the proposed algorithm can embed secret data efficiently with better visual quality.

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How to Cite
Ahmad, M., EL-Emam, N. N., & AL-Azawi, A. F. (2022). Improved Deep Hiding/Extraction Algorithm to Enhance the Payload Capacity and Security Level of Hidden Information. International Journal of Communication Networks and Information Security (IJCNIS), 13(3). (Original work published December 25, 2021)
Research Articles
Author Biography

Nameer N. EL-Emam, Department of Computer Science, Philadelphia University

Nameer N. EL-Emam has completed his PhD with honourĀ 1997. He works as an assistant professor in the Computer Science Department at Basra University. In 1998, he joins the Department of Computer Science, Philadelphia University, as an assistant professor. and then he got anĀ associate professor in 2010. Now he is a full professor at the same university, and he works as a chair of computer science department and the deputy dean of the faculty of Information Technology, Philadelphia University. His research interest includes Computer Simulation with an intelligent system, Parallel Algorithms, and Soft computing using Neural Network, GA, ACO, and PSO for many kinds of applications like Image Processing, Sound Processing, Fluid Flow, and Computer Security (Steganography).