Adversarial Robustness for Machine Learning - Brossura

Chen; Hsieh

Brossura

ISBN 10: 0128240202 ISBN 13: 9780128240205

Casa editrice: Academic Press, 2022

Riassunto

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.

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Informazioni sugli autori

Pin-Yu Chen: Dr. Pin-Yu Chen is a 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 building trustworthy machine learning systems. At IBM Research, he received the honor of IBM Master Inventor and several research accomplishment awards, including an IBM Master Inventor and IBM Corporate Technical Award in 2021. His research works contribute to IBM open-source libraries including Adversarial Robustness Toolbox (ART 360) and AI Explainability 360 (AIX 360). He has published more than 40 papers related to trustworthy machine learning at major AI and machine learning conferences, given tutorials at AAAI’22, IJCAI’21, CVPR(’20,’21), ECCV’20, ICASSP’20, KDD’19, and Big Data’18, and organized several workshops for adversarial machine learning. He received a NeurIPS 2017 Best Reviewer Award, and was also the recipient of the IEEE GLOBECOM 2010 GOLD Best Paper 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.

Dalla quarta di copertina

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. Adversarial robustness has become one of the mainstream topics in machine learning with much research carried out, while many companies have started to incorporate security and robustness into their systems.

Adversarial Robustness for Machine Learning Models summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense, and veri?cation. It contains 6 parts: The ?rst three parts cover adversarial attack, veri?cation, and defense, mainly focusing on image classi?cation applications, which is the standard benchmark considered in the adversarial robustness community. It then discusses 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 this area, which can be a good reference for conducting future research. It could also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning.

Dettagli dell'edizione

• Editore: Academic Press
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 0128240202
• ISBN 13: 9780128240205
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 298
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile