Adversarial Robustness for Machine Learning

by Pin-Yu Chen, Cho-Jui Hsieh

Overview

Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research.

In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems.

Product Details

ISBN-13:	9780128240205
Publisher:	Elsevier Science
Publication date:	08/25/2022
Pages:	298
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Pin-Yu Chen: Dr. Pin-Yu Chen is a principal research staff member at IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA. He is also the chief scientist of RPI-IBM AI Research Collaboration and PI of ongoing MIT-IBM Watson AI Lab projects. Dr. Chen received his Ph.D. degree in electrical engineering and computer science from the University of Michigan, Ann Arbor, USA, in 2016. Dr. Chen’s recent research focuses on adversarial machine learning and robustness of neural networks. His long-term research vision is to build trustworthy machine learning systems. He is a co-author of the book “Adversarial Robustness for Machine Learning”. At IBM Research, he received several research accomplishment awards, including IBM Master Inventor, IBM Corporate Technical Award, and IBM Pat Goldberg Memorial Best Paper. His research contributes to IBM open-source libraries including Adversarial Robustness Toolbox (ART 360) and AI Explainability 360 (AIX 360). He has published more than 50 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at NeurIPS’22, AAAI(’22,’23), IJCAI’21, CVPR(’20,’21,’23), ECCV’20, ICASSP(’20,’22,’23), KDD’19, and Big Data’18, and organized several workshops for adversarial machine learning. He received the IEEE GLOBECOM 2010 GOLD Best Paper Award and UAI 2022 Best Paper Runner-Up Award.

Dr. Cho-Jui Hsieh is an Assistant Professor at the UCLA Computer Science department. His research focuses on developing algorithms and optimization techniques for training large-scale and robust machine learning models. He publishes in top-tier machine learning conferences including ICML, NIPS, KDD, ICLR and has won the best paper awards at KDD 2010, ICDM 2012, ICPP 2018, best paper finalist at AISEC 2017 and best student paper finalist at SC 2019. He is also the author of several widely used open source machine learning software including LIBLINEAR. His work has been cited by more than 13,000 times on Google scholar.

Table of Contents

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 verification
10. Adversarial training
11. Certified 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

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From the Publisher

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