<span>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 ar
Adversarial Robustness for Machine Learning
β Scribed by Chen, Pin-Yu;Hsieh, Cho-Jui;; Cho-Jui Hsieh
- Publisher
- Elsevier Science & Technology
- Year
- 2022
- Tongue
- English
- Category
- Library
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
<p><span>This book demonstratesΒ the optimal adversarial attacks against several important signal processing algorithms.Β Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal proc
M L Algorithms (2022) [Li et al] [9783031163753]
The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompa
Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, le
<p><span>The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has bee