Deep Learning for Multimedia Processing Applications. Volume One, Image Security and Intelligent Systems for Multimedia Processing

Deep Learning for Multimedia Processing Applications Volume One, Image Security and Intelligent Systems for Multimedia Processing Volume One, Image Security and Intelligent Systems for Multimedia Processing /

Detalles Bibliográficos
Otros Autores: Bhatti, Uzair Aslam, 1986- editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2024]
Edición:First edition
Materias:
Multimedia systems.
Deep learning (Machine learning)
Digital video.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009826135606719
Tabla de Contenidos:
  • Intro
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Contents
  • Contributors
  • Chapter 1 A Novel Robust Watermarking Algorithm for Encrypted Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition
  • 1.1 Introduction
  • 1.2 Basic Theory
  • 1.2.1 Discrete Wavelet Transform (DWT)
  • 1.2.2 Non-Subsampled Shearlet Transform (NSST)
  • 1.2.3 Matrix Schur Decomposition
  • 1.2.4 Chaos Encryption System
  • 1.3 Proposed Algorithm
  • 1.3.1 Medical Image Encryption
  • 1.3.2 Feature Extraction
  • 1.3.3 Embed Watermark
  • 1.3.4 Extraction of Watermark
  • 1.4 Experiments and Analysis of Results
  • 1.4.1 Simulation Experiment
  • 1.4.2 Attacks Results
  • 1.4.3 Contrastion to Plaintext Domain Algorithm
  • 1.4.4 Contrastion to Other Encrypted Algorithms
  • 1.5 Conclusion
  • References
  • Chapter 2 Robust Zero Watermarking Algorithm for Encrypted Medical Images Based on SUSAN-DCT
  • 2.1 Introduction
  • 2.2 Literature Review
  • 2.3 Basic Theory and Proposed Algorithm
  • 2.3.1 SUSAN Edge Detection
  • 2.3.2 Hu Moments
  • 2.3.3 Logical Mapping
  • 2.3.4 Proposed Algorithm
  • 2.4 Experiment and Results
  • 2.4.1 Evaluation Parameter
  • 2.4.2 Experimental Setup
  • 2.4.3 Results and Analysis
  • 2.5 Conclusion
  • References
  • Chapter 3 Robust Zero Watermarking Algorithm for Encrypted Medical Volume Data Based on PJFM and 3D-DCT
  • 3.1 Introduction
  • 3.2 The Fundamental Theory
  • 3.2.1 Pseudo Jacobi-Fourier Moment
  • 3.2.2 D-DCT and 3D-IDCT
  • 3.2.3 Logistic Mapping
  • 3.3 The Proposed Method
  • 3.3.1 Medical Volume Data Encryption
  • 3.3.2 Feature Extraction
  • 3.3.3 Watermark Encryption and Embedding
  • 3.3.4 Watermark Extraction and Decryption
  • 3.4 Experimental Results and Performance Evaluation
  • 3.4.1 Simulation Experiment
  • 3.4.2 Attacks Results
  • 3.4.3 Comparison with Unencrypted Algorithm
  • 3.5 Conclusion
  • References.
  • Chapter 4 Robust Zero Watermarking Algorithm for Medical Images Based on BRISK and DCT
  • 4.1 Introduction
  • 4.2 Fundamental Theory
  • 4.2.1 BRISK Feature Extraction Algorithm
  • 4.2.2 Discrete Cosine Transform (DCT)
  • 4.2.3 Logistic Mapping
  • 4.3 Proposed Algorithm
  • 4.3.1 Medical Image Feature Extraction
  • 4.3.2 Watermark Encryption
  • 4.3.3 Embed Watermark
  • 4.3.4 Watermark Extraction and Decryption
  • 4.4 Experiments and Results
  • 4.4.1 Test Different Images
  • 4.4.2 Conventional Attacks
  • 4.4.3 Geometric Attacks
  • 4.4.4 Compare with Other Algorithms
  • 4.5 Conclusion
  • References
  • Chapter 5 Robust Color Images Zero-Watermarking Algorithm Based on Stationary Wavelet Transform and Daisy Descriptor
  • 5.1 Introduction
  • 5.2 Literature Review
  • 5.3 Material and Techniques
  • 5.3.1 Daisy Descriptor
  • 5.3.2 Stationary Wavelet Transform
  • 5.3.3 Tent Chaotic Map
  • 5.3.4 Proposed Algorithm
  • 5.4 Experiment and Results
  • 5.4.1 Evaluation Parameter
  • 5.4.2 Feasibility Analysis
  • 5.4.3 Results and Analysis
  • 5.5 Conclusion
  • References
  • Chapter 6 Robust Multi-watermarking Algorithm Based on DarkNet53
  • 6.1 Introduction
  • 6.2 Basic Theory
  • 6.2.1 DarkNet53
  • 6.2.2 Discrete Cosine Transform
  • 6.2.3 Logistic Map
  • 6.3 Proposed Algorithm
  • 6.3.1 Improvement of DarkNet53 Network Model
  • 6.3.2 Encryption of Watermark
  • 6.3.3 Watermark Embedding
  • 6.3.4 Extraction of a Watermark
  • 6.3.5 Decryption of a Watermark
  • 6.4 Experimental Results and Analysis
  • 6.4.1 Performance
  • 6.4.2 Reliability Analysis
  • 6.4.3 Traditional Attack
  • 6.4.4 Geometric Attack
  • 6.5 Conclusion
  • References
  • Chapter 7 Robust Multi-watermark Algorithm for Medical Images Based on SqueezeNet Transfer Learning
  • 7.1 Introduction
  • 7.2 Fundamental Theory
  • 7.2.1 SqueezeNet Neural Network
  • 7.2.2 Transfer Learning.
  • 7.2.3 SPM Composite Chaotic Mapping
  • 7.3 Proposed Algorithm
  • 7.3.1 Retraining the Network
  • 7.3.2 Watermark Encryption
  • 7.3.3 Generation and Extraction of Zero Watermark
  • 7.3.4 Decryption of Watermark
  • 7.4 Experimental Results
  • 7.4.1 Evaluation Metrics
  • 7.4.2 Discrimination Testing
  • 7.4.3 Robustness Testing
  • 7.4.4 Comparison
  • 7.5 Conclusion
  • References
  • Chapter 8 Deep Learning Applications in Digital Image Security: Latest Methods and Techniques
  • 8.1 Introduction
  • 8.2 Background
  • 8.2.1 Basic Model
  • 8.2.2 Learning-based Model
  • 8.3 Classification of Digital Watermarking
  • 8.3.1 Divided by Characteristics
  • 8.3.2 Divided by Detection Method
  • 8.3.3 Divided by Hidden Domain
  • 8.3.4 Other Classifications
  • 8.4 Performance Evaluation and Algorithms
  • 8.4.1 Performance Evaluation
  • 8.4.2 Algorithms
  • 8.5 Attacks
  • 8.5.1 Robust Attack
  • 8.5.2 No Attack
  • 8.5.3 Explaining the Attack
  • 8.6 Learning-based Watermarking
  • 8.7 Applications of Learning-based Watermarking
  • 8.7.1 Medical Field
  • 8.7.2 Remote-sensing Field
  • 8.7.3 Map Copyright
  • 8.7.4 Copyright Protection
  • 8.7.5 Content Authentication
  • 8.7.6 Infringement Tracking
  • 8.7.7 Radio Monitoring
  • 8.7.8 Copy Control
  • 8.7.9 Electronic Field
  • 8.8Conclusion
  • Funding
  • References
  • Chapter 9 Image Fusion Techniques and Applications for Remote Sensing and Medical Images
  • 9.1 Introduction
  • 9.2 Rule of Image Fusion
  • 9.3 Levels of Image Fusion
  • 9.3.1 Pixel-Level Image Fusion
  • 9.3.2 Feature-Level Image Fusion
  • 9.3.3 Decision-Level Image Fusion
  • 9.4 Image Fusion Methods
  • 9.4.1 Spatial Domain Fusion Methods
  • 9.4.2 Frequency Domain Fusion Methods
  • 9.4.3 Deep Learning Methods
  • 9.5 Techniques for the Assessment of Image Fusion Quality
  • 9.6 Image Fusion Categorization
  • 9.6.1 Single Sensor
  • 9.6.2 Multi-Sensors.
  • 9.6.3 Multiview Fusion
  • 9.6.4 Multimodal Fusion
  • 9.6.5 Multi-Focus Fusion
  • 9.6.6 Multi-Temporal Fusion
  • 9.7 Image Fusion Applications
  • 9.7.1 Medical Image Fusion
  • 9.7.2 Remote-Sensing Image Fusion
  • 9.7.3 Visible-Infrared Fusion
  • 9.7.4 Multi-Focus Image Fusion
  • 9.8 Conclusion
  • References
  • Chapter 10Detecting Phishing URLs through Deep Learning Models
  • 10.1 Introduction
  • 10.2 DL Models Used in Cybersecurity
  • 10.2.1 Convolutional Neural Network
  • 10.2.2 Recurrent Neural Networks
  • 10.2.3 Long Short-Term Memory
  • 10.2.4 Deep Belief Networks
  • 10.2.5 Multi-Layer Perceptron
  • 10.2.6 Generative Adversarial Network
  • 10.3 Metrics
  • 10.3.1 Accuracy
  • 10.3.2 Precision
  • 10.3.3 Recall (Sensitivity)
  • 10.3.4 F1 Score
  • 10.3.5 Confusion Matrix
  • 10.4 Application of Deep Learning in Cybersecurity Use Cases
  • 10.4.1 Intrusion Detection System
  • 10.4.2 Malware Detection
  • 10.4.3 Botnet Detection
  • 10.4.4 Network Traffic Identification
  • 10.4.5 Credit Card Fraud Detection
  • 10.5 Existing Work Related to Phishing URL Detection Using DL Models
  • 10.6 Conclusion
  • References
  • Chapter 11 Augmenting Multimedia Analysis: A Fusion of Deep Learning with Differential Privacy
  • 11.1 Introduction
  • 11.2 Multimedia Data and Crowdsensing Privacy Concerns
  • 11.3 Deep Learning and Privacy Risks
  • 11.3.1 Privacy Attacks in Deep Learning Pipeline
  • 11.4 Algorithms for Preserving Privacy
  • 11.5 The Differential Privacy Distributions
  • 11.6 How Differential Privacy Fuses With Deep Learning
  • 11.7 Methodology: Exploring the Intersection of Multimedia Data With Deep Learning and Privacy in Literature
  • 11.7.1 Preserving-Privacy Image Analysis
  • 11.7.2 Preserving-Privacy Video Analysis
  • 11.7.3 Preserving-Privacy With Other Methods
  • 11.8 Discussion
  • 11.9 Conclusion
  • References.
  • Chapter 12 Multi-classification Deep Learning Models for Detecting Multiple Chest Infection Using Cough and Breath Sounds
  • 12.1 Introduction
  • 12.2 Literature Review
  • 12.3 Materials and Methods
  • 12.3.1 Proposed Study Flow for the Diagnosis of Multiple Chest Infections
  • 12.3.2 Data Set Description
  • 12.3.3 Using SMOTE Tomek to Balance the Data Set
  • 12.3.4 Deep Learning Classifiers
  • 12.3.5 Proposed Model
  • 12.3.6 Dense Block of Proposed Model
  • 12.3.7 Model Evaluations
  • 12.4 Results and Discussion
  • 12.4.1 Experimental Setup
  • 12.4.2 Accuracy Comparison of Proposed Model with Baseline Models
  • 12.4.3 AUC Comparison with Baseline Models
  • 12.4.4 Comparison with Baseline Models Using Precision
  • 12.4.5 Comparison of DMCIC_Net with Baseline Models Using Recall
  • 12.4.6 F1-Score Comparison with Baseline Models
  • 12.4.7 Comparison of Proposed Model with Baseline Models Using Loss
  • 12.4.8 Comparison of ROC with Current Models
  • 12.4.9 AU(ROC) Extension for Multiclass Comparison Against Recent Models
  • 12.4.10 Comparison of DMCIC_Net with Six Models Using a Confusion Matrix
  • 12.4.11 Comparison of the Proposed Model with State of the Art
  • 12.4.12 Discussion
  • 12.5 Conclusion
  • References
  • Chapter 13 Classifying Traffic Signs Using Convolutional Neural Networks Based on Deep Learning Models
  • 13.1 Introduction
  • 13.2 How Does a Model Learn?
  • 13.2.1 Types of Machine Learning
  • 13.2.2 Tasks Performed by Machine Learning
  • 13.2.3 Depth of Machine Learning
  • 13.3 Deep Learning
  • 13.3.1 Training of Deep Learning Models
  • 13.3.2 Algorithms Used to Train Deep Learning Models
  • 13.4 Classification of Images Using a Convolutional Neural Network
  • 13.4.1 Classifying Images Using Traditional Methods
  • 13.4.2 Image Classification Using CNN
  • 13.4.3 Overview of CNN Models Used for Image Classification.
  • 13.5 Classifying Traffic Signs Using Convolutional Neural Network.

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