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Adversarial Robustness For Machine Learning Pinyu Chen Chojui Hsieh

Name: Adversarial Robustness For Machine Learning Pinyu Chen Chojui Hsieh
SKU: BELL-46098934
Price: 31.00 USD
Availability: InStock
Rating: 4.7 (76 reviews)

SKU: BELL-46098934

$ 31.00 ~~$ 45.00~~ (-31%)

4.7

76 reviews

Adversarial Robustness For Machine Learning Pinyu Chen Chojui Hsieh instant download after payment.

Publisher: Academic Press

File Extension: PDF

File size: 10.15 MB

Pages: 275

Author: Pin-Yu Chen, Cho-Jui Hsieh

ISBN: 9780128240205, 0128240202

Language: English

Year: 2022

Product desciption

Adversarial Robustness For Machine Learning Pinyu Chen Chojui Hsieh by Pin-yu Chen, Cho-jui Hsieh 9780128240205, 0128240202 instant download after payment.

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
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.