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Towards the Exploration and Improvem...

Leone, Stephen.

Towards the Exploration and Improvement of Generative Adversarial Attacks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards the Exploration and Improvement of Generative Adversarial Attacks.
作者:	Leone, Stephen.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2020
面頁冊數:	130 p.
附註:	Source: Masters Abstracts International, Volume: 82-05.
附註:	Advisor: Fontaine, Fred L.
Contained By:	Masters Abstracts International82-05.
標題:	Artificial intelligence.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27994576
ISBN:	9798684653148

Towards the Exploration and Improvement of Generative Adversarial Attacks.
Leone, Stephen.

Towards the Exploration and Improvement of Generative Adversarial Attacks. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 130 p.

Source: Masters Abstracts International, Volume: 82-05.

Thesis (M.E.)--The Cooper Union for the Advancement of Science and Art, 2020.

This item must not be sold to any third party vendors.

Adversarial examples represent a major security threat for emerging technologies that use machine learning models to make important decisions. Adversarial examples are typically crafted by performing gradient descent on the target model, but recently, researchers have started to look at methods to generate adversarial examples without requiring access to the target model at decision time. This thesis investigates the place that generative attacks have in the machine learning security threat landscape and improvements that can be made on existing attacks. By evaluating attacks and defenses on image classiﬁcation problems, we found that generative attackers are most relevant in black box settings where query access is given to the attacker before test time. We evaluate generative methods in these settings as standalone attacks and we give examples of how generative attacks can reduce the number of queries or amount of time required to produce a successful adversarial example by narrowing the search space of existing optimization algorithms.

ISBN: 9798684653148Subjects--Topical Terms:

194058
Artificial intelligence.
Subjects--Index Terms:

Adversarial Examples

Towards the Exploration and Improvement of Generative Adversarial Attacks.
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506 $a This item must not be sold to any third party vendors.
520 $a Adversarial examples represent a major security threat for emerging technologies that use machine learning models to make important decisions. Adversarial examples are typically crafted by performing gradient descent on the target model, but recently, researchers have started to look at methods to generate adversarial examples without requiring access to the target model at decision time. This thesis investigates the place that generative attacks have in the machine learning security threat landscape and improvements that can be made on existing attacks. By evaluating attacks and defenses on image classiﬁcation problems, we found that generative attackers are most relevant in black box settings where query access is given to the attacker before test time. We evaluate generative methods in these settings as standalone attacks and we give examples of how generative attacks can reduce the number of queries or amount of time required to produce a successful adversarial example by narrowing the search space of existing optimization algorithms.
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