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
Attribute Inference Techniques
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- Model Inversion Attacks That Exploit Confidence Information and Other Properties of Machine Learning Models, Matthew Fredrikson, Somesh Jha, and Thomas Ristenpart, 2015 Proceedings of the 2015 ACM SIGSAC Conference on Computer and Communications Security (CCS) (ACM) DOI: 10.1145/2810103.2813733 - A foundational paper demonstrating attribute inference (model inversion) attacks, explicitly discussing the use of confidence scores and model outputs to infer sensitive attributes.
- Deep Learning with Differential Privacy, Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang, 2016 Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (ACM) DOI: 10.1145/2976749.2978318 - A landmark paper that introduced a practical method for training deep neural networks with differential privacy, directly addressing privacy leakage concerns including inference attacks.
- Exploiting the learning process of deep neural networks for attribute inference attacks, Ziqi Liu, Mingxuan Liu, Xiaojie Cui, Peng He, Jianmin Wang, Mingming Fan, Jing Li, 2021 Neural Computing and Applications, Vol. 33 (Springer) DOI: 10.1007/s00521-020-05597-2 - This paper explores how the internal learning process of deep neural networks can be exploited to perform attribute inference, offering a deeper understanding of vulnerability.