| Description |
1 online resource (347 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- Contributors -- Part I Secure AI/ML Systems: Attack Models -- 1 Machine Learning Attack Models -- 1.1 Introduction -- 1.2 Background -- 1.2.1 Notation -- 1.2.2 Support Vector Machines -- 1.2.3 Neural Networks -- 1.3 White-Box Adversarial Attacks -- 1.3.1 L-BGFS Attack -- 1.3.2 Fast Gradient Sign Method -- 1.3.3 Basic Iterative Method -- 1.3.4 DeepFool -- 1.3.5 Fast Adaptive Boundary Attack -- 1.3.6 Carlini and Wagner's Attack -- 1.3.7 Shadow Attack -- 1.3.8 Wasserstein Attack |
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1.4 Black-Box Adversarial Attacks -- 1.4.1 Transfer Attack -- 1.4.2 Score-Based Black-Box Attacks -- ZOO Attack -- Square Attack -- 1.4.3 Decision-Based Attack -- Boundary Attack -- HopSkipJump Attack -- Spatial Transformation Attack -- 1.5 Data Poisoning Attacks -- 1.5.1 Label Flipping Attacks -- 1.5.2 Clean Label Data Poisoning Attack -- Feature Collision Attack -- Convex Polytope Attack and Bullseye Polytope Attack -- 1.5.3 Backdoor Attack -- 1.6 Conclusions -- Acknowledgment -- Note -- References |
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2 Adversarial Machine Learning: A New Threat Paradigm for Next-Generation Wireless Communications -- 2.1 Introduction -- 2.1.1 Scope and Background -- 2.2 Adversarial Machine Learning -- 2.3 Challenges and Gaps -- 2.3.1 Development Environment -- 2.3.2 Training and Test Datasets -- 2.3.3 Repeatability, Hyperparameter Optimization, and Explainability -- 2.3.4 Embedded Implementation -- 2.4 Conclusions and Recommendations -- References -- 3 Threat of Adversarial Attacks to Deep Learning: A Survey -- 3.1 Introduction -- 3.2 Categories of Attacks -- 3.2.1 White-Box Attacks -- FGSM-based Method |
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JSMA-based Method -- 3.2.2 Black-Box Attacks -- Mobility-based Approach -- Gradient Estimation-Based Approach -- 3.3 Attacks Overview -- 3.3.1 Attacks On Computer-Vision-Based Applications -- 3.3.2 Attacks On Natural Language Processing Applications -- 3.3.3 Attacks On Data Poisoning Applications -- 3.4 Specific Attacks In The Real World -- 3.4.1 Attacks On Natural Language Processing -- 3.4.2 Attacks Using Data Poisoning -- 3.5 Discussions and Open Issues -- 3.6 Conclusions -- References -- 4 Attack Models for Collaborative Deep Learning -- 4.1 Introduction -- 4.2 Background |
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4.2.1 Deep Learning (DL) -- Convolution Neural Network -- 4.2.2 Collaborative Deep Learning (CDL) -- Architecture -- Collaborative Deep Learning Workflow -- 4.2.3 Deep Learning Security and Collaborative Deep Learning Security -- 4.3 Auror: An Automated Defense -- 4.3.1 Problem Setting -- 4.3.2 Threat Model -- Targeted Poisoning Attacks -- 4.3.3 AUROR Defense -- 4.3.4 Evaluation -- 4.4 A New CDL Attack: Gan Attack -- 4.4.1 Generative Adversarial Network (GAN) -- 4.4.2 GAN Attack -- Main Protocol -- 4.4.3 Experiment Setups -- Dataset -- System Architecture -- Hyperparameter Setup |
| Summary |
Today AI and Machine/Deep Learning have become the hottest areas in the information technology. This book aims to provide a complete picture on the challenges and solutions to the security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks |
| Notes |
Description based upon print version of record |
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4.4.4 Evaluation |
| Subject |
Artificial intelligence
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Computer networks -- Security measures
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Computer security -- Data processing
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Deep learning (Machine learning) -- Security measures
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Machine learning -- Security measures
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Artificial intelligence.
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Computer networks -- Security measures.
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| Form |
Electronic book
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| Author |
Hei, Xiali
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| ISBN |
9781000878899 |
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1000878899 |
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