Adaptive Matrix Pattern Steganography on RGB Images




Steganography, Steganalysis, Matrix Pattern


Almost all spatial domain image steganography methods rely on modifying the Least Significant Bits (LSB) of each pixel to minimize the visual distortions. However, these methods are susceptible to LSB blind attacks and quantitative steganalyses.

This paper presents an adaptive spatial domain image steganography algorithm for hiding digital media based on matrix patterns, named “Adaptive Matrix Pattern” (AMP). The AMP method increases the security of the steganography scheme of largely hidden messages since it adaptively generates a unique codebook matrix pattern for each ASCII character in each image block. Therefore, each ASCII character gets a different codebook matrix pattern even in different regions of the same image. Moreover, it uses a preprocessing algorithm to identify the most suitable image blocks for hiding purposes. The resulting stego-images are robust against LSB blind attacks since the middle bits of green and blue channels generate matrix patterns and hiding secrets, respectively. Experimental results show that AMP is robust against quantitative steganalyses. Additionally, the quality of stego-images, based on the peak signal-to-noise ratio metric, remains high in both stego-RGB-image and in the stego-blue-channel. Finally, the AMP method provides a high hiding capacity, up to 1.33 bits per pixel.


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Author Biographies

Amirfarhad Nilizadeh, University of Central Florida, Orlando, Florida, USA

Amirfarhad Nilizadeh is a Ph.D. Candidate in Computer Science at University of Central Florida (UCF). Currently, he is a research and teaching assistant at UCF. His research interests focus on formal methods, software engineering, and cybersecurity. He was an intern researcher at CyLab Security & Privacy Institute at Carnegie Mellon University in summer 2018. He was a university lecturer professor at Azad University for three years, 2014–2016.

Shirin Nilizadeh, University of Texas Dallas, Dallas, Texas, USA

Shirin Nilizadeh is Assistant Professor at the Department of ComputerScience and Engineering, the University of Texas at Arlington. She received her Ph.D. in Security Informatics from the Indiana University Bloomington (IUB). For her dissertation on Privacy-aware Decentralized Architectures for Socially Networked Systems, she received a two-year fellowship from the school of Informatics and Computing at IUB. Following her doctorate, she held post-doctoral positions in CNets at IUB from 2014–2015, in SecLabat University of California Santa Barbara from 2015–2017, and then in Cy-Lab at Carnegie Mellon University from 2017–2018. Her research focuses on security and privacy in the context of systems and social networks using techniques from machine learning and big data analytics.

Wojciech Mazurczyk, Warsaw University of Technology, Warsaw, Poland

Wojciech Mazurczyk received the M.Sc., Ph.D., and D.Sc degrees in telecommunications from the Warsaw University of Technology (WUT), Poland, in 2004, 2009, and 2014, respectively. He is currently a University Professor with the Institute of Computer Science at WUT and a Researcher at the Faculty of Mathematics and Computer Science, FernUniversitaet, Germany. Since 2016, he has been the Editor-in-Chief of the Journal of Cyber Security and Mobility and since 2018 as an Associate Editor for the IEEE Transactions on Information Forensics and Security.

Cliff Zou, University of Central Florida, Orlando, Florida, USA

Cliff Zou received his Ph.D. degree from the Department of Electrical & Computer Engineering, the University of Massachusetts at Amherst, in 2005, and MS and BS degree from the University of Science & Technology of China in 1999 and 1996, respectively. Currently, he is an Associate Professor in the Department of Computer Science and the Program Coordinator of the Digital Forensics Master’s program at the University of Central Florida. His research interests focus on cybersecurity and computer networking. He has published more than 80 peer-reviewed research papers and has obtained more than 6100 Google Scholar Citations. He is a Senior Member of the IEEE.

Gary T. Leavens, University of Central Florida, Orlando, Florida, USA

Gary T. Leavens is a professor in the department of Computer Science at UCF. After joining UCF in August 2007, he became associate chair in 2008, and was department chair from 2010-2021. He led a successful faculty cluster proposal in cyber security and privacy. His research is in formal methods. Previously Dr. Leavens was a professor of Computer Science at Iowa State University in Ames, Iowa, where he started in 1989, after receiving his doctorate from MIT. Before graduate studies at MIT, he worked at Bell Telephone Laboratories in Denver Colorado as a member of technical staff.


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How to Cite

Nilizadeh A, Nilizadeh S, Mazurczyk W, Zou C, Leavens GT. Adaptive Matrix Pattern Steganography on RGB Images. JCSANDM [Internet]. 2021 Aug. 30 [cited 2023 Sep. 30];11(1):1–28. Available from: