Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models-Reference-Cited by-同舟云学术

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Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models

Published:2023-05 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Security and Privacy Workshops (SPW)
language:
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Author:

Baracaldo Nathalie¹,Ahmed Farhan¹,Eykholt Kevin¹,Zhou Yi¹,Priya Shriti¹,Lee Taesung¹,Kadhe Swanand¹,Tan Mike²,Polavaram Sridevi²,Suggs Sterling³,Gao Yuyang⁴,Slater David³

Affiliation:

1. IBM Research

2. The MITRE Corporation

3. Two Six Technologies

4. Emory University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10188616/10188617/10188619.pdf?arnumber=10188619

Reference29 articles.

1. Badnets: Identifying vulnera-bilities in the machine learning model supply chain;gu;ar Xiv preprint,2017

2. Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks;schwarzschild;Int Conference on Machine Learning,2021

3. Fairness through awareness

Cited by 2 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. An AI blue team playbook;Assurance and Security for AI-enabled Systems;2024-06-07

2. A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions;Archives of Computational Methods in Engineering;2024-05-08

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