Adversarial Robustness for Machine Learning
Language: English
Published by Academic Press, 2022
- Softcover
- New
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- Title
- Adversarial Robustness for Machine Learning
- Author
- Chen, Pin-Yu; Hsieh, Cho-Jui
- Publisher
- Academic Press
- Publication year
- 2022
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 0128240202
- ISBN 13
- 9780128240205
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.
- 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
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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