1st Edition

Deep Learning based applications for Multimedia Processing Applications Volume 1 and 2

    792 Pages 152 Color & 67 B/W Illustrations
    by CRC Press

    Deep Learning for Multimedia Processing is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing.

    Divided into two volumes, Volume One begins by introducing the fundamental concepts of deep learning, providing readers with a solid foundation to understand its relevance in multimedia processing. Volumes Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos.

    Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts.

    Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.

    Volume 1

    1. A Novel Robust Watermarking Algorithm for Encrypted Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition
    Meng Yang, Jingbing Li1, Uzair Aslam Bhatti, Yiyi Yuan, and QinQing Zhang

    2. Robust Zero Watermarking Algorithm for Encrypted Medical Images Based on SUSAN-DCT
    Jingbing Li, Qinqing Zhang, Meng Yang, and Yiyi Yuan

    3. Robust Watermarking Algorithm for Encrypted Medical Volume Data Based on PJFM and 3D-DCT
    Lei Cao, Jingbing Li, and Uzair Aslam Bhatti

    4. Robust Zero Watermarking Algorithm for Medical Images Based on BRISK and DCT
    Fangchun Dong, Jingbing Li, and Uzair Aslam Bhatti

    5. Robust Color Images Zero Watermarking Algorithm Based on Smooth Wavelet Transform and Daisy descriptor
    Yiyi Yuan, Jingbing Li1, Uzair Aslam Bhatti, Meng Yang, and Qinqing Zhang

    6. Robust Multi-Watermarking Agorithm based on Darknet53 Convolutional Neural Network
    Dekai Li, Jingbing Li, and Uzair Aslam Bhatti

    7. Robust Multi-Watermarking Algorithm for Medical Images Based on Squeezenet Transfer Learning
    Pengju Zhang, Jingbing Li, and Uzair Aslam Bhatti

    8. Deep Learning Applications in Digital Image Security: Latest Methods And Techniques
    Saqib Ali Nawaz, Jingbing Li, Uzair Aslam Bhatti, Muhammad Usman Shoukat, and Raza Muhammad Ahmad

    9. Image Fusion Techniques and Applications for Remote Sensing Images and Medical Images
    Emadalden Alhatami, MengXing Huang, and Uzair Aslam Bhatti

    10. Detecting Phishing URLs Through Deep Learning Models
    Shah Noor, Sibghat Ullah Bazai, Saima Tareen, and Shafi Ullah

    11. Augmenting Multimedia Analysis: A Fusion of Deep Learning with Differential Privacy
    Iqra Tabassum and Dr. Sibghat Ullah Bazai

    12. Multi-Classification Deep Learning Models for Detecting Multiple Chest Infection using Cough and Breath Sound
    Amna Tahir, Hassaan Malik, and Muhammad Umar Chaudhry

    13. Classifying Traffic Signs using Convolutional Neural Networks based on Deep Learning Models
    Saira Akram, Sibghat Ullah Bazai, and Shah Marjan

    14. Cloud-Based Intrusion Detection System using Deep Neural Network and Human-in-the-Loop Decision-Making
    Hootan Alavizadeh and Hooman Alavizadeh


    Uzair Aslam Bhatti was born in 1986. He received the Ph.D. degree in information and communication engineering, Hainan University, Haikou, Hainan, in 2019. He completed his Postdoctoral from Nanjing Normal University, Nanjing, China in implementing Clifford algebra algorithms in analysing the geospatial data using artificial intelligence (AI). He is currently working as Associate Professor in School of information and communication engineering in Hainan University. His areas of specialty include AI, machine learning, and image processing. He is serving as guest editor of various journals including Frontier in Plant Science, Frontier in Environmental Science, Computer Materials and Continua, Plos One, IEEE Access etc and has reviewed many IEEE Transactions and Elsevier journals.

    Jingbing Li, is a doctor, professor, doctoral supervisor, and the Vice President of Hainan Provincial Invention Association. He has been awarded honorary titles of Leading Talents in Hainan Province, Famous Teaching Teachers in Hainan Province, Outstanding Young and Middle-aged Backbone Teachers in Hainan Province, and Excellent Teachers in Baosteel. He has also won the second prize of Hainan Provincial Science and Technology Progress Award three times (the first completer twice, the second completer once). He has obtained 13 authorized national invention patents, published 5 monographs such as medical image digital watermarking, published more than 80 SCI / EI retrieved academic papers (including 22 SCI retrieved papers) as the first author or corresponding author. He has presided over 2 projects of the National Natural Science Foundation of China, and 5 projects of Hainan Province's key research and development projects and Hainan Province's international scientific and technological cooperation projects.

    Dr. Huang Mengxing is Dean of the School of Information, at Hainan University. He has occupied many roles, such as the leader of the talent team of "Smart Service", the chief scientist of the National Key R&D Program, a member of the Expert Committee of Artificial Intelligence and Blockchain of the Science and Technology Committee of the Ministry of Education, the executive director of the Postgraduate Education Branch of the China Electronics Education Society, and the Computer Professional Teaching Committee of the Ministry of Education, among others. His main research areas include big data and intelligent information processing, multi-source information perception and fusion, artificial intelligence and intelligent services, etc. In recent years, he has published more than 230 academic papers as the first author and corresponding author, has obtained 36 invention patents authorized by the state, and 96 software copyrights; published 4 monographs and translated 2 books. He has won first prize and second prize of Hainan Provincial Science and Technology Progress Award as the first person who completed it; won 2 Hainan Provincial Excellent Teaching Achievement Awards and Excellent Teacher Award. He has presided over and undertaken more than 30 national, provincial, and ministerial-level projects, such as national key research and development plan projects, national science and technology support plans, and National Natural Science Foundation projects.

    Sibghat Ullah Bazai completed his undergraduate and graduate studies in Computer Engineering at the Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS) in Quetta, Pakistan. He received his Ph.D. (IT) in Cyber Security from Massey University in Auckland, New Zealand in 2020. As part of his research, he is interested in applying cyber security, identifying diseases with deep learning, automating exams with natural language processing, developing local language sentiment datasets, and planning smart cities. Sibghat is a guest editor and reviewer for several journals' special issues in MDPI, Hindawi, CMC, PlosOne, Frontier, and others.

    Muhammad Aamir received the Bachelor of Engineering degree in Computer Systems Engineering from Mehran University of Engineering & Technology Jamshoro, Sindh, Pakistan, in 2008, the Master of Engineering degree in Software Engineering from Chongqing University, China, in 2014, and the PhD degree in Computer Science and Technology from Sichuan University, Chengdu, China, in 2019. He is currently an Associate Professor at the Department of Computer, Huanggang Normal University, China. His main research interests include pattern recognition, computer vision, image processing, deep learning, and fractional calculus.