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

朝圣者

铁砧

Eric J. Kostelich,Ernest Barreto spam, phishing, and malware detectors trained to distinguish between benign and malicious instances, with adversaries manipulating the nature of the objects, such as introducing clever word misspellings or substitutions of code regions, in order to be misclassified as benign.

cornucopia

售穴

Kai Ma,Pei Liu,Jie Yang,Xinping Guanthey take place . learning, when the learned model is in operational use. We now turn to another broad class of attacks which target the learning . by tampering directly with data used for training these.

取消

Kai Ma,Pei Liu,Jie Yang,Xinping Guan. as follows. We start with the pristine training dataset . of . labeled examples. Suppose that an unknown proportion α of the dataset . is then corrupted 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 c

顾客

Kai Ma,Pei Liu,Jie Yang,Xinping Guannatural language processing [Goodfellow et al., 2016]. This splash was soon followed by a series of illustrations of fragility of deep neural network models to small . changes to inputs. While initially these were seen largely as robustness tests rather than modeling actual attacks, the language of

发生

Dna262

Book 2018 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 safety cri

nostrum

Decision Support via Fuzzy Technologyike, trying to maintain productivity despite external threats, and .the bad guys—who spread malware, send spam and phishing emails, hack into vulnerable computing devices, steal data, or execute denial-of-service attacks, for whatever malicious ends they may have.

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