Record link: 
http://hdl.handle.net/20.500.12708/191919
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Title: 
Adversarial ML for DNNs, CapsNets, and SNNs at the Edge
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Citation: 
Marchisio, A., Hanif, M. A., & Shafique, M. (2023). Adversarial ML for DNNs, CapsNets, and SNNs at the Edge. In S. Pasricha & M. Shafique (Eds.), Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Use Cases and Emerging Challenges (pp. 463–496). Springer. https://doi.org/10.1007/978-3-031-40677-5_18
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Publisher DOI: 
10.1007/978-3-031-40677-5_18
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Publication Type: 
Book Contribution - Edited Volume Contribution
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Language: 
English
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Authors: 
Marchisio, Alberto 
Hanif, Muhammad Abdullah 
Shafique, Muhammad 
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Organisational Unit: 
E191-02 - Forschungsbereich Embedded Computing Systems 
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Published in: 
Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing : Use Cases and Emerging Challenges 
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ISBN: 
978-3-031-40677-5
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Date (published): 
7-Oct-2023
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Number of Pages: 
34
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Publisher: 
Springer, Cham
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Peer reviewed: 
Yes
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Keywords: 
machine learning security; adversarial attacks; robustness; deep neural networks; capsule networks; spiking neural networks
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Abstract: 
Recent studies have shown that Machine Learning (ML) algorithm suffers from several vulnerability threats. Among them, adversarial attacks represent one of the most critical issues. This chapter provides an overview of the ML vulnerability challenges, with a focus on the security threats for Deep Neural Networks, Capsule Networks, and Spiking Neural Networks. Moreover, it discusses the current trends and outlooks on the methodologies for enhancing the ML models’ robustness.
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
Book Contribution

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