Adversarial Robustness for Machine Learning

Adversarial Robustness for Machine Learning

1st Edition - September 1, 2022

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  • Authors: Pin-Yu Chen, Cho-Jui Hsieh
  • Paperback ISBN: 9780128240205

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Description

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.

Key Features

  • 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

Readership

Computer scientists and engineers at Universities; R&D engineers in industry

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

Product details

  • No. of pages: 425
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: September 1, 2022
  • Imprint: Academic Press
  • Paperback ISBN: 9780128240205

About the Authors

Pin-Yu Chen

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.

Affiliations and Expertise

Research Staff member, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA

Cho-Jui Hsieh

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.

Affiliations and Expertise

Assistant Professor, UCLA Computer Science Department, USA

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