Adversarial Deep Learning: A Survey on Adversarial Attacks and Defense Mechanisms on Image Classification

The popularity of adapting deep neural networks (DNNs) in solving hard problems has increased substantially. Specifically, in the field of computer vision, DNNs are becoming a core element in developing many image and video classification and recognition applications. However, DNNs are vulnerable to...

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Vydané v:IEEE access Ročník 10; s. 1
Hlavní autori: Khamaiseh, Samer Y., Bagagem, Derek, Al-Alaj, Abdullah, Mancino, Mathew, Alomari, Hakam W.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
adversarial examples
adversarial perturbations
artificial intelligence
Artificial neural networks
Classification
Computational modeling
Computer vision
Deep learning
Deep neural networks
Defense mechanisms
Image classification
Machine learning
Machine learning algorithms
Neural networks
Object recognition
Perturbation
Perturbation methods
Security
Training data
ISSN:2169-3536, 2169-3536
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Shrnutí:The popularity of adapting deep neural networks (DNNs) in solving hard problems has increased substantially. Specifically, in the field of computer vision, DNNs are becoming a core element in developing many image and video classification and recognition applications. However, DNNs are vulnerable to adversarial attacks, in which, given a well-trained image classification model, a malicious input can be crafted by adding mere perturbations to misclassify the image. This phenomena raise many security concerns in utilizing DNNs in critical life applications which attracts the attention of academic and industry researchers. As a result, multiple studies have proposed discussing novel attacks that can compromise the integrity of state-of-the-art image classification neural networks. The raise of these attacks urges the research community to explore countermeasure methods to mitigate these attacks and increase the reliability of adapting DDNs in different major applications. Hence, various defense strategies have been proposed to protect DNNs against adversarial attacks. In this paper, we thoroughly review the most recent and state-of-the-art adversarial attack methods by providing an in-depth analysis and explanation of the working process of these attacks. In our review, we focus on explaining the mathematical concepts and terminologies of the adversarial attacks, which provide a comprehensive and solid survey to the research community. Additionally, we provide a comprehensive review of the most recent defense mechanisms and discuss their effectiveness in defending DNNs against adversarial attacks. Finally, we highlight the current challenges and open issues in this field as well as future research directions.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3208131