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
Metrics for Adversarial Robustness
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- Towards Evaluating the Robustness of Neural Networks, Nicholas Carlini and David A. Wagner, 2017 2017 IEEE Symposium on Security and Privacy (SP) (IEEE Computer Society) DOI: 10.1109/SP.2017.49 - Presents strong adversarial attacks (C&W attacks) for various $L_p$ norms ($L_0, L_1, L_2$) and highlights the significance of evaluating robustness by finding the minimum perturbation required for misclassification.
- Certified Adversarial Robustness via Randomized Smoothing, Jeremy Cohen, Elan Rosenfeld, Zico Kolter, 2019 Proceedings of the 36th International Conference on Machine Learning, Vol. 97 (PMLR) DOI: 10.48550/arXiv.1902.02923 - Introduces randomized smoothing as a general framework for constructing provably robust classifiers and provides a method for quantifying certified adversarial robustness.