TRAttack: Text Rewriting Attack Against Text Retrieval

Junshuai Song, Jiangshan Zhang, Jifeng Zhu, Mengyun Tang, Yong Yang


Abstract
Text retrieval has been widely-used in many online applications to help users find relevant information from a text collection. In this paper, we study a new attack scenario against text retrieval to evaluate its robustness to adversarial attacks under the black-box setting, in which attackers want their own texts to always get high relevance scores with different users’ input queries and thus be retrieved frequently and can receive large amounts of impressions for profits. Considering that most current attack methods only simply follow certain fixed optimization rules, we propose a novel text rewriting attack (TRAttack) method with learning ability from the multi-armed bandit mechanism. Extensive experiments conducted on simulated victim environments demonstrate that TRAttack can yield texts that have higher relevance scores with different given users’ queries than those generated by current state-of-the-art attack methods. We also evaluate TRAttack on Tencent Cloud’s and Baidu Cloud’s commercially-available text retrieval APIs, and the rewritten adversarial texts successfully get high relevance scores with different user queries, which shows the practical potential of our method and the risk of text retrieval systems.
Anthology ID:
2022.repl4nlp-1.20
Volume:
Proceedings of the 7th Workshop on Representation Learning for NLP
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Spandana Gella, He He, Bodhisattwa Prasad Majumder, Burcu Can, Eleonora Giunchiglia, Samuel Cahyawijaya, Sewon Min, Maximilian Mozes, Xiang Lorraine Li, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Laura Rimell, Chris Dyer
Venue:
RepL4NLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
191–203
Language:
URL:
https://aclanthology.org/2022.repl4nlp-1.20
DOI:
10.18653/v1/2022.repl4nlp-1.20
Bibkey:
Cite (ACL):
Junshuai Song, Jiangshan Zhang, Jifeng Zhu, Mengyun Tang, and Yong Yang. 2022. TRAttack: Text Rewriting Attack Against Text Retrieval. In Proceedings of the 7th Workshop on Representation Learning for NLP, pages 191–203, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
TRAttack: Text Rewriting Attack Against Text Retrieval (Song et al., RepL4NLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.repl4nlp-1.20.pdf
Video:
 https://aclanthology.org/2022.repl4nlp-1.20.mp4