A Visual Analytics Framework for Adversarial Text Generation
This work addresses the challenge of improving adversarial text quality for researchers and practitioners in machine learning, though it is incremental as it builds upon existing attack algorithms.
The paper tackles the problem of adversarial text generation by introducing a visual analytics framework that facilitates human intervention to correct poor semantics or syntax in automatically generated adversarial examples, enabling users to review and edit in real-time for diagnosing robustness issues or comparing classifiers.
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 adversarial 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.