2020/01/22 by Ying Xu, Xu, Ying, Xu Zhong +5 · 1 citation
Computer Science · #Advanced Malware Detection Techniques #Adversarial Robustness in Machine Learning #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2001.07820
openalex publication_date 2020/01/22 · openalex created_date 2020/01/30 · openalex updated_date 2026/07/28
An adversarial example is an input transformed by small perturbations that machine learning models consistently misclassify. While there are a number of methods proposed to generate adversarial examples for text data, it is not trivial to assess the quality of these adversarial examples, as minor perturbations (such as changing a word in a sentence) can lead to a significant shift in their meaning, readability and classification label. In this paper, we propose an evaluation framework consisting of a set of automatic evaluation metrics and human evaluation guidelines, to rigorously assess the quality of adversarial examples based on the aforementioned properties. We experiment with six benchmark attacking methods and found that some methods generate adversarial examples with poor readability and content preservation. We also learned that multiple factors could influence the attacking performance, such as the length of the text inputs and architecture of the classifiers.