Deep Learning for Multimedia Processing Applications. Volume One, Image Security and Intelligent Systems for Multimedia Processing / edited by Uzair Aslam Bhatti [and four others].

Format:

Book

Contributor:

Bhatti, Uzair Aslam, 1986- editor.

Language:

English

Subjects (All):

Multimedia systems.

Deep learning (Machine learning).

Digital video.

Physical Description:

1 online resource (302 pages)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, [2024]

Summary:

This book 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.

Contents:

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

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

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

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

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

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

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

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

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

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

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.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Other Format:

Print version: Bhatti, Uzair Aslam Deep Learning for Multimedia Processing Applications

ISBN:

9781003827962

OCLC:

1415895060