Fraud’s Bargain Attacks to Textual Classifiers via Metropolis-Hasting Sampling (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v37i13.27005Keywords:
Adversarial Learning, Textual Attack, Natural Language Processing.Abstract
Recent studies on adversarial examples expose vulnerabilities of natural language processing (NLP) models. Existing techniques for generating adversarial examples are typically driven by deterministic heuristic rules that are agnostic to the optimal adversarial examples, a strategy that often results in attack failures. To this end, this research proposes Fraud's Bargain Attack (FBA), which utilizes a novel randomization mechanism to enlarge the searching space and enables high-quality adversarial examples to be generated with high probabilities. FBA applies the Metropolis-Hasting algorithm to enhance the selection of adversarial examples from all candidates proposed by a customized Word Manipulation Process (WMP). WMP perturbs one word at a time via insertion, removal, or substitution in a contextual-aware manner. Extensive experiments demonstrate that FBA outperforms the baselines in terms of attack success rate and imperceptibility.Downloads
Published
2023-09-06
How to Cite
Ni, M., Sun, Z., & Liu, W. (2023). Fraud’s Bargain Attacks to Textual Classifiers via Metropolis-Hasting Sampling (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16290-16291. https://doi.org/10.1609/aaai.v37i13.27005
Issue
Section
AAAI Student Abstract and Poster Program