ShanghaiTech University Knowledge Management System
SHARP: Search-Based Adversarial Attack for Structured Prediction | |
2022 | |
会议录名称 | FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: NAACL 2022 - FINDINGS |
页码 | 950-961 |
发表状态 | 已发表 |
摘要 | Adversarial attack of structured prediction models faces various challenges such as the difficulty of perturbing discrete words, the sentence quality issue, and the sensitivity of outputs to small perturbations. In this work, we introduce SHARP, a new attack method that formulates the black-box adversarial attack as a search-based optimization problem with a specially designed objective function considering sentence fluency, meaning preservation and attacking effectiveness. Additionally, three different searching strategies are analyzed and compared, i.e., Beam Search, Metropolis- Hastings Sampling, and Hybrid Search. We demonstrate the effectiveness of our attacking strategies on two challenging structured prediction tasks: part-of-speech (POS) tagging and dependency parsing. Through automatic and human evaluations, we show that our method performs a more potent attack compared with pioneer arts. Moreover, the generated adversarial examples can be used to successfully boost the robustness and performance of the victim model via adversarial training. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings. |
关键词 | Computational linguistics Forecasting Attack methods Black boxes Objective functions Optimization problems Prediction modelling Quality issues Search based optimizations Search-based Small perturbations Structured prediction |
会议名称 | 2022 Findings of the Association for Computational Linguistics: NAACL 2022 |
会议地点 | Seattle, WA, United states |
会议日期 | July 10, 2022 - July 15, 2022 |
收录类别 | EI |
语种 | 英语 |
出版者 | Association for Computational Linguistics (ACL) |
EI入藏号 | 20223712713466 |
EI主题词 | Syntactics |
EI分类号 | 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory |
原始文献类型 | Conference article (CA) |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/229869 |
专题 | 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_屠可伟组 |
作者单位 | 1.School of Information Science and Technology, ShanghaiTech University, China; 2.Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, China; 3.Beijing Institute for General Artificial Intelligence, China |
第一作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Zhang, Liwen,Jia, Zixia,Han, Wenjuan,et al. SHARP: Search-Based Adversarial Attack for Structured Prediction[C]:Association for Computational Linguistics (ACL),2022:950-961. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 |
修改评论
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。