Conference proceeding
LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.2307-2315
ACM Conferences
KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
08/14/2022
DOI: 10.1145/3534678.3539357
Abstract
Generating text adversarial examples in the hard-label setting is a more realistic and challenging black-box adversarial attack problem, whose challenge comes from the fact that gradient cannot be directly calculated from discrete word replacements. Consequently, the effectiveness of gradient-based methods for this problem still awaits improvement. In this paper, we propose a gradient-based optimization method named LeapAttack to craft high-quality text adversarial examples in the hard-label setting. To specify, LeapAttack employs the word embedding space to characterize the semantic deviation between the two words of each perturbed substitution by their difference vector. Facilitated by this expression, LeapAttack gradually updates the perturbation direction and constructs adversarial examples in an iterative round trip: firstly, the gradient is estimated by transforming randomly sampled word candidates to continuous difference vectors after moving the current adversarial example near the decision boundary; secondly, the estimated gradient is mapped back to a new substitution word based on the cosine similarity metric. Extensive experimental results show that in the general case LeapAttack can efficiently generate high-quality text adversarial examples with the highest semantic similarity and the lowest perturbation rate in the hard-label setting.
Details
- Title: Subtitle
- LeapAttack: Hard-Label Adversarial Attack on Text via Gradient-Based Optimization
- Creators
- Muchao Ye - Pennsylvania State UniversityJinghui Chen - Pennsylvania State UniversityChenglin Miao - University of GeorgiaTing Wang - Pennsylvania State UniversityFenglong Ma - Pennsylvania State University
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.2307-2315
- Conference
- KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Series
- ACM Conferences
- DOI
- 10.1145/3534678.3539357
- Language
- English
- Date published
- 08/14/2022
- Academic Unit
- Computer Science
- Record Identifier
- 9984696709802771
Metrics
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