Adversarial Robustness for Machine Learning

Adversarial Robustness for Machine Learning 1st Edition
by Pin-Yu Chen (Author), Cho-Jui Hsieh (Author)
Publisher Finelybook 出版社:Academic Press; 1st edition (September 8, 2022)
Language 语言:English
pages 页数:298 pages
ISBN-10 书号:0128240202
ISBN-13 书号:9780128240205

Book 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.


下载地址:

Adversarial Robustness for Machine Learning 9780128240205.rar (访问密码: 1024)

下载地址 Download隐藏内容需1积分,VIP免费,请先 !没有帐号? 注 册 一个!
觉得文章有用就打赏一下
未经允许不得转载:finelybook » Adversarial Robustness for Machine Learning

评论 抢沙发

  • 昵称 (必填)
  • 邮箱 (必填)
  • 网址

觉得文章有用就打赏一下

非常感谢你的打赏,我们将继续给力更多优质内容,让我们一起创建更加美好的网络世界!

支付宝扫一扫打赏

微信扫一扫打赏