Titlebook: Adversarial Machine Learning; Yevgeniy Vorobeychik,Murat Kantarcioglu Book 2018 Springer Nature Switzerland AG 2018

故意釣到白楊

1939-4608 de machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are

不給啤

Kai Ma,Pei Liu,Jie Yang,Xinping Guancused on supervised learning, our discussion will be restricted to this setting. Additionally, we deal with an important special case of such attacks in the context of deep learning separately in Chapter 8.

Musket

Kai Ma,Pei Liu,Jie Yang,Xinping Guanpted arbitrarily (i.e., both feature vectors and labels may be corrupted), resulting in a corrupted dataset .. The goal is to learn a model . on the corrupted data . which is nearly as good (in terms of, say, prediction accuracy) as a model . learned on pristine data ..

鈍劍

Kai Ma,Pei Liu,Jie Yang,Xinping Guanmodels to small . changes to inputs. While initially these were seen largely as robustness tests rather than modeling actual attacks, the language of . has since often been taken more literally, for example, with explicit connections to security and safety applications.

disparage

https://doi.org/10.1007/978-3-319-46367-4ep learning methods have received. While we devote an entire chapter solely to adversarial deep learning, we emphasize that proper understanding of these necessitates a broader look at adversarial learning that the rest of the book provides.

僵硬

Defending Against Decision-Time Attacks,cused on supervised learning, our discussion will be restricted to this setting. Additionally, we deal with an important special case of such attacks in the context of deep learning separately in Chapter 8.

泥瓦匠

incision

爭(zhēng)論

The Road Ahead,ep learning methods have received. While we devote an entire chapter solely to adversarial deep learning, we emphasize that proper understanding of these necessitates a broader look at adversarial learning that the rest of the book provides.

吊胃口