Adversarial Robustness for Machine Learning

A complete overview of the field of adversarial robustness for machine learning models

Pin-Yu Chen (Author), Cho-Jui Hsieh (Author)

9780128240205

Paperback, published 25 August 2022

298 pages, Approx. 100 illustrations
28.6 x 21.6 x 2 cm, 0.45 kg

Approx.284 pages

1. White-box attack
2. Soft-label Black-box Attack
3. Decision-based attack
4. Attack Transferibility
5. Attacks in the physical world
6. Convex relaxation Framework
7. Layer-wise relaxation (primal algorithms)
8. Dual approach
9. Probabilistic veri?cation
10. Adversarial training
11. Certi?ed defense
12. Randomization
13. Detection methods
14. Robustness of other machine learning models beyond neural networks
15. NLP models
16. Graph neural network
17. Recommender systems
18. Reinforcement Learning
19. Speech models
20. Multi-modal models
21. Backdoor attack and defense
22. Data poisoning attack and defense
23. Transfer learning
24. Explainability and interpretability
25. Representation learning
26. Privacy and watermarking

Subject Areas: Machine learning [UYQM], Artificial intelligence [UYQ]