Strengthening Deep Neural Networks. Making AI Less Susceptible to Adversarial Trickery

Spis treści ebooka

• Preface
  • Who Should Read This Book
  • How This Book Is Organized
  • Conventions Used in This Book
  • Using Code Examples
  • The Mathematics in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. An Introduction to Fooling AI
• 1. Introduction
  • A Shallow Introduction to Deep Learning
  • A Very Brief History of Deep Learning
  • AI Optical Illusions: A Surprising Revelation
  • What Is Adversarial Input?
    • Adversarial Perturbation
    • Unnatural Adversarial Input
    • Adversarial Patches
    • Adversarial Examples in the Physical World
  • The Broader Field of Adversarial Machine Learning
  • Implications of Adversarial Input
• 2. Attack Motivations
  • Circumventing Web Filters
  • Online Reputation and Brand Management
  • Camouflage from Surveillance
  • Personal Privacy Online
  • Autonomous Vehicle Confusion
  • Voice Controlled Devices
• 3. Deep Neural Network (DNN) Fundamentals
  • Machine Learning
  • A Conceptual Introduction to Deep Learning
  • DNN Models as Mathematical Functions
    • DNN Inputs and Outputs
    • DNN Internals and Feed-Forward Processing
    • How a DNN Learns
  • Creating a Simple Image Classifier
• 4. DNN Processing for Image, Audio, and Video
  • Image
    • Digital Representation of Images
    • DNNs for Image Processing
    • Introducing CNNs
  • Audio
    • Digital Representation of Audio
    • DNNs for Audio Processing
    • Introducing RNNs
    • Speech Processing
  • Video
    • Digital Representation of Video
    • DNNs for Video Processing
  • Adversarial Considerations
  • Image Classification Using ResNet50
• II. Generating Adversarial Input
• 5. The Principles of Adversarial Input
  • The Input Space
    • Generalizations from Training Data
    • Experimenting with Out-of-Distribution Data
  • Whats the DNN Thinking?
  • Perturbation Attack: Minimum Change, Maximum Impact
  • Adversarial Patch: Maximum Distraction
  • Measuring Detectability
    • A Mathematical Approach to Measuring Perturbation
    • Considering Human Perception
  • Summary
• 6. Methods for Generating Adversarial Perturbation
  • White Box Methods
    • Searching the Input Space
    • Exploiting Model Linearity
    • Adversarial Saliency
    • Increasing Adversarial Confidence
    • Variations on White Box Approaches
  • Limited Black Box Methods
  • Score-Based Black Box Methods
  • Summary
• III. Understanding the Real-World Threat
• 7. Attack Patterns for Real-World Systems
  • Attack Patterns
    • Direct Attack
    • Replica Attack
    • Transfer Attack
    • Universal Transfer Attack
  • Reusable Patches and Reusable Perturbation
  • Bringing It Together: Hybrid Approaches and Trade-offs
• 8. Physical-World Attacks
  • Adversarial Objects
    • Object Fabrication and Camera Capabilities
    • Viewing Angles and Environment
  • Adversarial Sound
    • Audio Reproduction and Microphone Capabilities
    • Audio Positioning and Environment
  • The Feasibility of Physical-World Adversarial Examples
• IV. Defense
• 9. Evaluating Model Robustness to Adversarial Inputs
  • Adversarial Goals, Capabilities, Constraints, and Knowledge
    • Goals
    • Capabilities, Knowledge, and Access
  • Model Evaluation
    • Empirically Derived Robustness Metrics
    • Theoretically Derived Robustness Metrics
  • Summary
• 10. Defending Against Adversarial Inputs
  • Improving the Model
    • Gradient Masking
    • Adversarial Training
    • Out-of-Distribution Confidence Training
    • Randomized Dropout Uncertainty Measurements
  • Data Preprocessing
    • Preprocessing in the Broader Processing Chain
    • Intelligently Removing Adversarial Content
  • Concealing the Target
  • Building Strong Defenses Against Adversarial Input
    • Open Projects
    • Taking a Holistic View
• 11. Future Trends: Toward Robust AI
  • Increasing Robustness Through Outline Recognition
  • Multisensory Input
  • Object Composition and Hierarchy
  • Finally
• A. Mathematics Terminology Reference
• Index

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