Advanced Adversarial Machine Learning
Chapter 1: Foundations of Adversarial ML Security
Review of Machine Learning Security Vulnerabilities
Threat Models in Machine Learning
Attack Surfaces: Training vs. Inference
Mathematical Formulation of Adversarial Examples
Taxonomy of Adversarial Attacks
Overview of Defense Strategies
Chapter 2: Advanced Evasion Attacks
Gradient-Based Attacks: FGSM, BIM, PGD Analysis
Optimization-Based Attacks: Carlini & Wagner Methods
Score-Based Attack Techniques
Decision-Based Attack Techniques
Transferability of Adversarial Examples
Attacking Ensemble Models
Implementing Evasion Attacks: Hands-on Practical
Chapter 3: Data Poisoning and Backdoor Attacks
Poisoning Attack Strategies: Availability vs Integrity
Targeted Data Poisoning Techniques
Backdoor Attack Mechanisms and Trigger Design
Clean-Label Poisoning Attacks
Analyzing Poisoning Impact on Model Training
Crafting Data Poisoning Attacks: Hands-on Practical
Chapter 4: Model Inference and Privacy Attacks
Membership Inference Attacks: Theory and Methods
Attribute Inference Techniques
Model Inversion and Reconstruction Attacks
Model Stealing: Functionality Extraction Methods
Relationship to Differential Privacy
Implementing Membership Inference: Hands-on Practical
Chapter 5: Robust Defense Mechanisms
Adversarial Training: Principles and Variations
Certified Defenses: Randomized Smoothing
Input Transformation Defenses
Gradient Masking and Obfuscation Issues
Defending Against Poisoning and Backdoors
Implementing Adversarial Training: Hands-on Practical
Chapter 6: Evaluating Model Robustness
Metrics for Adversarial Robustness
Benchmarking Tools and Frameworks
Adaptive Attacks: Evaluating Defenses Properly
Security Evaluations under Different Threat Models
Interpreting Robustness Evaluation Results
Setting up Robustness Benchmarks: Hands-on Practical
Chapter 7: Adversarial Examples in Specific Domains
Adversarial Attacks on Computer Vision Models
Generating Adversarial Text for NLP Models
Attacks on Reinforcement Learning Agents
Physical Adversarial Attacks
Domain-Specific Attack Considerations
Generating Adversarial Text: Practice
Security Evaluations under Different Threat Models
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- Towards Evaluating the Robustness of Neural Networks, Nicholas Carlini, David A. Wagner, 2017 IEEE Symposium on Security and Privacy (SP) (Institute of Electrical and Electronics Engineers (IEEE)) DOI: 10.1109/SP.2017.49 - Introduces a family of strong optimization-based white-box adversarial attacks (C&W attacks) for thoroughly evaluating model robustness.
- Decision-Based Adversarial Attacks, Wieland Brendel, Jonas Rauber, Matthias Bethge, 2018 Sixth International Conference on Learning Representations (ICLR 2018) DOI: 10.48550/arXiv.1712.04248 - Presents the Boundary Attack, a highly query-efficient decision-based black-box attack, relevant for evaluating robustness in settings with minimal attacker knowledge.
- Adversarial Machine Learning: A Survey, Xiaoyong Yuan, Pan He, Qiming Qu, Rui Xing, Xiaochun Cao, 2019 Journal of Parallel and Distributed Computing, Vol. 132 (Elsevier) DOI: 10.1016/j.jpdc.2019.04.011 - Provides a broad overview of adversarial machine learning, including discussions on different threat models, attack types (white-box, black-box, gray-box), and defense strategies.
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