A Study of the Attention Abnormality in Trojaned BERTs
Weimin Lyu, Songzhu Zheng, Tengfei Ma, Chao Chen
Abstract
Trojan attacks raise serious security concerns. In this paper, we investigate the underlying mechanism of Trojaned BERT models. We observe the attention focus drifting behavior of Trojaned models, i.e., when encountering an poisoned input, the trigger token hijacks the attention focus regardless of the context. We provide a thorough qualitative and quantitative analysis of this phenomenon, revealing insights into the Trojan mechanism. Based on the observation, we propose an attention-based Trojan detector to distinguish Trojaned models from clean ones. To the best of our knowledge, we are the first to analyze the Trojan mechanism and develop a Trojan detector based on the transformer’s attention.
BibTeX
@inproceedings{lyu-etal-2022-study,
title = "A Study of the Attention Abnormality in Trojaned {BERT}s",
author = "Lyu, Weimin and
Zheng, Songzhu and
Ma, Tengfei and
Chen, Chao",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.348/",
doi = "10.18653/v1/2022.naacl-main.348",
pages = "4727--4741"
}