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Digital Media Steganography

  • ID: 4911834
  • Book
  • July 2020
  • Region: Global
  • 400 Pages
  • Elsevier Science and Technology
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The common use of the Internet and cloud services in transmission of large amounts of data over open networks and insecure channels, exposes that private and secret data to serious situations. Ensuring the information transmission over the Internet is safe and secure has become crucial, consequently information security has become one of the most important issues of human communities because of increased data transmission over social networks. Digital Media Steganography: Principles, Algorithms, and Advances covers fundamental theories and algorithms for practical design, while providing a comprehensive overview of the most advanced methodologies and modern techniques in the field of steganography. The topics covered present a collection of high-quality research works written in a simple manner by world-renowned leaders in the field dealing with specific research problems. It presents the state-of-the-art as well as the most recent trends in digital media steganography.
  • Covers fundamental theories and algorithms for practical design which form the basis of modern digital media steganography
  • Provides new theoretical breakthroughs and a number of modern techniques in steganography
  • Presents the latest advances in digital media steganography such as using deep learning and artificial neural network as well as Quantum Steganography
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1. Introduction to digital image steganography 2. A color image steganography method based on ADPVD and HOG techniques 3. An improved method for high hiding capacity based on LSB and PVD 4. An efficient image steganography method using multi-objective differential evolution 5. Image steganography using add-sub based QVD and side match 6. A high capacity invertible steganography method for Stereo image 7. An adaptive and clustering-based steganographic method: Osteg 8. A steganography method based on decomposition of the Catalan numbers 9. A steganography approach for hiding privacy in video surveillance systems 10. Reversible steganography techniques: A survey 11. Quantum Steganography 12. Digital media steganalysis 13. Unsupervised steganographer identification via clustering and outlier detection 14. Deep Learning in steganography and steganalysis

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Hassaballah, Mahmoud
M. Hassaballah was born in 1974, Qena, Egypt. He received his B.Sc. degree in mathematics in 1997 and his M.Sc. degree in computer science in 2003, both from South Valley University, Egypt, and his Doctor of Engineering (D. Eng.) in computer science from Ehime University, Japan in 2011. He was a visiting scholar with the Department of Computer & Communication Science, Wakayama University, Japan in 2013 and GREAH laboratory, Le Havre Normandie University, France in 2019. He is currently an associate professor of computer science at the Faculty of Computers and Information, South Valley University, Egypt. He served as a reviewer for several Journals such as IEEE Transactions on Image Processing, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Parallel and Distributed Systems, Pattern Recognition, Pattern Recognition Letters, Egyptian Informatics Journal, IET Image Processing, IET Computer Vision, IET Biometrics, Journal of Real-Time Image Processing, The Computer Journal, Journal of Electronic Imaging, and Optical Engineering. He has published 5 books and over 50 research papers in refereed international journals and conferences. His research interests include feature extraction, object detection/recognition, artificial intelligence, biometrics, image processing, computer vision, machine learning, and data hiding.
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