Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats and Defenses-Reference-Cited by-同舟云学术

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Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats and Defenses

Published:2023 Issue: Volume: Page:1-13
ISSN:1545-5971
Container-title:IEEE Transactions on Dependable and Secure Computing
language:
Short-container-title:IEEE Trans. Dependable and Secure Comput.

Author:

Abdukhamidov Eldor¹^ORCID,Abuhamad Mohammed²^ORCID,Woo Simon S.³^ORCID,Chan-Tin Eric²^ORCID,Abuhmed Tamer¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, South Korea

2. Department of Computer Science, Loyola University, Chicago, USA

3. Department of Artificial Intelligence and Department of Applied Data Science, Sungkyunkwan University, Suwon, South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8858/4358699/10352932.pdf?arnumber=10352932

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Unmasking the Vulnerabilities of Deep Learning Models: A Multi-Dimensional Analysis of Adversarial Attacks and Defenses;2024 Silicon Valley Cybersecurity Conference (SVCC);2024-06-17

2. The Impact of Model Variations on the Robustness of Deep Learning Models in Adversarial Settings;2024 Silicon Valley Cybersecurity Conference (SVCC);2024-06-17

3. SingleADV: Single-Class Target-Specific Attack Against Interpretable Deep Learning Systems;IEEE Transactions on Information Forensics and Security;2024

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