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
Clean-Label Poisoning Attacks
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- Clean-label Data Poisoning Attacks, Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, Tom Goldstein, 2018 Advances in Neural Information Processing Systems 31, Vol. 31 (NeurIPS Proceedings) DOI: 10.5555/3327144.3327299 - Introduces the concept of clean-label poisoning attacks and demonstrates their effectiveness, including feature collision techniques.
- Black-box Clean-Label Poisoning Attacks on Machine Learning Models, Yassine O. F. Zhu, Bo Li, J. Zico Kolter, Dawn Song, 2019 Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (ACM) DOI: 10.1145/3292500.3330925 - Explores clean-label poisoning in the challenging black-box setting, where the attacker has limited knowledge of the model.
- Poisoning Attacks on Machine Learning: A Survey, Wei Cao, Zhipeng Wang, Jiawei Liu, Yicheng Wang, Han Liu, Hao Yu, 2021 ACM Computing Surveys, Vol. 54 (Association for Computing Machinery (ACM)) DOI: 10.1145/3476901 - A comprehensive review of data poisoning attacks and defenses, providing broad context for clean-label methods within the field.