作者: PACT 時間: 2025-3-21 20:20
Machine Learning Preliminaries,To keep this book reasonably self-contained, we start with some machine learning basics. Machine learning is often broadly divided into three major areas: supervised learning, unsupervised learning, and reinforcement learning. While in practice these divisions are not always clean, they provide a good point of departure for our purposes.作者: 多嘴 時間: 2025-3-22 03:46 作者: 無能的人 時間: 2025-3-22 07:11 作者: 鞠躬 時間: 2025-3-22 09:50
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.作者: 權(quán)宜之計 時間: 2025-3-22 15:40
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.作者: deforestation 時間: 2025-3-22 19:09 作者: engrossed 時間: 2025-3-22 21:39 作者: 煩躁的女人 時間: 2025-3-23 05:11
978-3-031-00452-0Springer Nature Switzerland AG 2018作者: 設(shè)施 時間: 2025-3-23 09:16 作者: 朝圣者 時間: 2025-3-23 13:07 作者: 鐵砧 時間: 2025-3-23 13:54
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.作者: cornucopia 時間: 2025-3-23 18:43 作者: 售穴 時間: 2025-3-23 23:58
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.作者: 取消 時間: 2025-3-24 03:09
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作者: 顧客 時間: 2025-3-24 08:13
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 作者: 發(fā)生 時間: 2025-3-24 11:49 作者: Dna262 時間: 2025-3-24 17:32
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作者: nostrum 時間: 2025-3-24 22:36
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.作者: GRATE 時間: 2025-3-25 01:08 作者: 故意釣到白楊 時間: 2025-3-25 03:28
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 作者: 不給啤 時間: 2025-3-25 10:04
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.作者: Musket 時間: 2025-3-25 12:25
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 ..作者: 鈍劍 時間: 2025-3-25 16:46
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.作者: disparage 時間: 2025-3-25 20:02
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.作者: 僵硬 時間: 2025-3-26 01:00
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.作者: 泥瓦匠 時間: 2025-3-26 04:43 作者: incision 時間: 2025-3-26 10:41 作者: 爭論 時間: 2025-3-26 15:01
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.作者: 吊胃口 時間: 2025-3-26 19:50 作者: harmony 時間: 2025-3-27 00:18
Categories of Attacks on Machine Learning,ulnerabilities centers around precise threat models. In this chapter, we present a general categorization of threat models, or attacks, in the context of machine learning. Our subsequent detailed presentation of the specific attacks will be grounded in this categorization.作者: anaerobic 時間: 2025-3-27 02:06 作者: Adrenaline 時間: 2025-3-27 06:47 作者: 小步走路 時間: 2025-3-27 10:52
Attacks at Decision Time, 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.作者: 逃避系列單詞 時間: 2025-3-27 14:27
Defending Against Decision-Time Attacks,follow-up question: how do we defend against such attacks? As most of the literature on robust learning in the presence of decision-time attacks is focused on supervised learning, our discussion will be restricted to this setting. Additionally, we deal with an important special case of such attacks 作者: Militia 時間: 2025-3-27 18:05
Data Poisoning Attacks,they 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.作者: vertebrate 時間: 2025-3-27 23:10 作者: insincerity 時間: 2025-3-28 05:35
Attacking and Defending Deep Learning,natural 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 作者: CALL 時間: 2025-3-28 08:55 作者: 黃瓜 時間: 2025-3-28 13:46
1939-4608 ontent of malicius objects they develop...The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adver978-3-031-00452-0978-3-031-01580-9Series ISSN 1939-4608 Series E-ISSN 1939-4616 作者: Hypomania 時間: 2025-3-28 17:11
Book 2018hine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop...The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adver作者: 接合 時間: 2025-3-28 19:46 作者: Customary 時間: 2025-3-29 01:10
