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
Score-Based Attack Techniques
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- Black-box Adversarial Attacks with Random Forests and Evolution Strategies, Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin, 2018 Proceedings of the 35th International Conference on Machine Learning (ICML), Vol. 80 - Presents a black-box attack method that leverages Evolution Strategies (ES) to efficiently estimate gradients for crafting adversarial examples, addressing the query complexity challenge.
- Adversarial Examples Are a Natural Consequence of Test Error in the Finite-Data Regime, Jonathan Uesato, Rohan Anil, Tom Filer, Alistair Gilbert, Luisa F. Richter, Clemens Rosenbaum, 2018 Advances in Neural Information Processing Systems (NeurIPS), Vol. 31 - Explores the fundamental properties of adversarial examples and demonstrates the use of Simultaneous Perturbation Stochastic Approximation (SPSA) as an effective score-based attack strategy.
- A Survey on Black-Box Adversarial Attacks and Defenses, Yanjiao Cao, Yuanzhang Liu, Lin Liu, Bo Li, Wenqi Zhou, Ming Li, 2021 ACM Computing Surveys, Vol. 54 (Association for Computing Machinery (ACM)) DOI: 10.1145/3477148 - Offers a comprehensive review of black-box adversarial attacks and defense mechanisms, providing context for various score-based techniques and their practical implications.