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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
Researchers, Scholars, Postgraduate Students, Students taking advanced course in related topics (e.g, Cryptography and data security). PhD Students worldwide and Developers interested in steganography and information hiding, and connected research disciplines, Senior Undergraduate Students who take advanced topics in network security or advanced topics in data security courses
- Introduction to Digital Image Steganography
2. Adaptive Steganography Method using HOG-based Pixel and ADPVD
3. High Payload Image Steganography using Exploiting Modification Direction
4. Quantum Steganography
5. High Capacity Invertible Steganography Method using 2D Histogram Shifting with EMD
6. Steganography Approach for Hiding Privacy in Video Surveillance Systems
7. Dynamic Steganography Method based on Cohort Intelligence Algorithm and Artificial Neural Network
8. Deep Learning in Steganography and Steganlysis
9. Non-dominated Sorting Genetic Algorithm (NSGA)-II based Image Steganography
- No. of pages:
- © Academic Press 2020
- 1st July 2020
- Academic Press
- Paperback ISBN:
Dr. Hassaballah received a B.Sc. degree in Mathematics in 1997, then M.Sc. degree in Computer Science in 2003, all from South Valley University, Egypt. In April 2008, he joined the lab of Intelligence Communication, Department of Electrical and Electronic Engineering and Computer Science, Ehime University, Japan as a PhD student, where he received a Doctor of Engineering (D. Eng.) in Computer Science on September 2011 for his work on facial features detection. Currently an associate professor of Computer Science with Department of Computer Science, Faculty of Computers and Information, South Valley University, Luxor, Egypt; Dr. Hassaballah is also acting Vice Dean for student affairs, Faculty of Computers and Information, South Valley University, Luxor, Egypt. He served as reviewer for several Journals such as: 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. His research interests include: feature extraction, object detection/recognition, face detection, content-based image retrieval, similarity measures, image processing, computer vision, and machine learning.
Faculty of Computers and Information, South Valley University, Luxor, Egypt