A Visual Analytics Framework for Adversarial Text Generation


Brandon Laughlin, Christopher Collins, Karthik Sankaranarayanan and Khalil El-Khatib


This paper presents a framework which enables a user to more easily make corrections to adversarial texts. While attack algorithms have been demonstrated to automatically build adversaries, changes made by the algorithms can often have poor semantics or syntax. Our framework is designed to facilitate human intervention by aiding users in making corrections. The framework extends existing attack algorithms to work within an evolutionary attack process paired with a visual analytics loop. Using an interactive dashboard a user is able to review the generation process in real time and receive suggestions from the system for edits to be made. The adversaries can be used to both diagnose robustness issues within a single classifier or to compare various classifier options. With the weaknesses identified, the framework can also be used as a first step in mitigating adversar- ial threats. The framework can be used as part of further research into defense methods in which the adversarial examples are used to evaluate new countermeasures. We demonstrate the framework with a word swapping attack for the task of sentiment classification.


This research was supported by the Communications Security Establishment, the government of Canada’s national cryptologic agency and the Natural Sciences and Engineering Research Council of Canada (NSERC).


  • [IMG]
    B. Laughlin, C. Collins, K. Sankaranarayanan, and K. El-Khatib, “A Visual Analytics Framework for Adversarial Text Generation,” in Proceedings of the IEEE Symposium on Visualization for Cyber Security (VizSec), 2019.
    [Bibtex] [PDF]

      author =    {Brandon Laughlin and Christopher Collins and Karthik Sankaranarayanan and Khalil El-Khatib},
      title =    {A Visual Analytics Framework for Adversarial Text Generation},
      booktitle =   {Proceedings of the IEEE Symposium on Visualization for Cyber Security (VizSec)},
      year = 2019


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