Adversarial Robustness for Machine Learning
Published on 20. August 2022
298 pages
978-0-12-824257-5 (ISBN)
Description
Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and veri¿cation. Sections cover adversarial attack, veri¿cation and defense, mainly focusing on image classi¿cation 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.
- Summarizes the whole field of adversarial robustness for Machine learning models
- Provides a clearly explained, self-contained reference
- Introduces formulations, algorithms and intuitions
- Includes applications based on adversarial robustness
More details
Other editions
Additional editions
Pin-Yu Chen | Cho-Jui Hsieh
Adversarial Robustness for Machine Learning
Book
08/2022
Academic Press
€105.17
Shipment within 15-20 days
Persons
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 ¿nalist at AISEC 2017 and best student paper ¿nalist 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.
Content
1. White-box attack2. Soft-label Black-box Attack3. Decision-based attack4. Attack Transferibility5. Attacks in the physical world6. Convex relaxation Framework7. Layer-wise relaxation (primal algorithms)8. Dual approach9. Probabilistic veri¿cation10. Adversarial training11. Certi¿ed defense12. Randomization13. Detection methods14. Robustness of other machine learning models beyond neural networks15. NLP models16. Graph neural network17. Recommender systems18. Reinforcement Learning19. Speech models20. Multi-modal models21. Backdoor attack and defense22. Data poisoning attack and defense23. Transfer learning24. Explainability and interpretability25. Representation learning26. Privacy and watermarking
