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Adversary Aware Continual Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Adversary Aware Continual Learning./
Author:	Umer, Muhammad.
Description:	1 online resource (129 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: A.
Contained By:	Dissertations Abstracts International84-12A.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30525471click for full text (PQDT)
ISBN:	9798379693831

Adversary Aware Continual Learning.
Umer, Muhammad.

Adversary Aware Continual Learning. - 1 online resource (129 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: A.

Thesis (Ph.D.)--Rowan University, 2023.

Includes bibliographical references

Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter such an insidious attack where, we use the attacker's primary strength - hiding the back-door pattern by making it imperceptible to humans - against it and propose to learn a perceptible (stronger) pattern (also during the training) that can overpower the attacker's imperceptible (weaker) pattern. We demonstrate the effectiveness of the proposed defensive mechanism through various commonly used replay-based (both generative and exact replay-based) continual learning algorithms using CIFAR-10, CIFAR-100, and MNIST benchmark datasets. Most noteworthy, we show that our proposed defensive framework considerably improves the robustness of continual learning algorithms with ZERO knowledge of the attacker's target task, attacker's target class, shape, size, and location of the attacker's pattern. The proposed defensive framework also does not depend on the underlying continual learning algorithm. We term our proposed defensive framework as Adversary Aware Continual Learning (AACL).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379693831Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Continual learning approachesIndex Terms--Genre/Form:

542853
Electronic books.

Adversary Aware Continual Learning.
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502 $a Thesis (Ph.D.)--Rowan University, 2023.
504 $a Includes bibliographical references
520 $a Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter such an insidious attack where, we use the attacker's primary strength - hiding the back-door pattern by making it imperceptible to humans - against it and propose to learn a perceptible (stronger) pattern (also during the training) that can overpower the attacker's imperceptible (weaker) pattern. We demonstrate the effectiveness of the proposed defensive mechanism through various commonly used replay-based (both generative and exact replay-based) continual learning algorithms using CIFAR-10, CIFAR-100, and MNIST benchmark datasets. Most noteworthy, we show that our proposed defensive framework considerably improves the robustness of continual learning algorithms with ZERO knowledge of the attacker's target task, attacker's target class, shape, size, and location of the attacker's pattern. The proposed defensive framework also does not depend on the underlying continual learning algorithm. We term our proposed defensive framework as Adversary Aware Continual Learning (AACL).
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538 $a Mode of access: World Wide Web
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