Piccolo: Exposing Complex Backdoors in NLP Transformer Models-Reference-Cited by-同舟云学术

Piccolo: Exposing Complex Backdoors in NLP Transformer Models

Published:2022-05 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Symposium on Security and Privacy (SP)
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Author:

Liu Yingqi¹,Shen Guangyu¹,Tao Guanhong¹,An Shengwei¹,Ma Shiqing²,Zhang Xiangyu¹

Affiliation:

1. Purdue University

2. Rutgers University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9833550/9833558/09833579.pdf?arnumber=9833579

Reference101 articles.

1. T-miner: A generative approach to defend against trojan attacks on dnn-based text classification;azizi;30th USENIX Security Symposium ( USENIX Security 21),2021

2. Detecting backdoors in neural networks using novel feature-based anomaly detection;fu;arXiv preprint arXiv 2011 00177,2020

3. Spectral signatures in backdoor attacks;tran;Advances in neural information processing systems,2018

4. SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems

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