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
Taxonomy of Adversarial Attacks
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- Explaining and Harnessing Adversarial Examples, Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy, 2015 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1412.6572 - Introduces the Fast Gradient Sign Method (FGSM) and discusses fundamental properties of adversarial examples, laying groundwork for white-box evasion attacks.
- Towards Evaluating the Robustness of Neural Networks, Nicholas Carlini and David A. Wagner, 2017 IEEE Symposium on Security and Privacy (SP) (IEEE) DOI: 10.1109/SP.2017.49 - Introduces powerful optimization-based white-box adversarial attacks (C&W attacks) and details methods for generating both targeted and untargeted adversarial examples.
- Practical Black-Box Attacks against Machine Learning Systems using Adversarial Examples, Nicolas Papernot, Patrick McDaniel, Arjund Sinha, Z. Berkay Celik, 2017 ACM Asia Conference on Computer and Communications Security (ASIACCS) (ACM) DOI: 10.1145/3052973.3053009 - Demonstrates transferability of adversarial examples and introduces black-box attack strategies using substitute models.
- Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models, Wieland Brendel, Jonas Rauber, and Matthias Bethge, 2018 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.1712.04248 - Introduces the Boundary Attack, a highly effective decision-based black-box attack that only requires access to the model's final label predictions.