Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models

Understand

This work proposes a structured approach to baselining a model, identifying attack vectors, and securing the machine learning models after deployment.

• This method for securing each model post deployment is called the BAD (Build, Attack, and Defend) Architecture.
• Two implementations of the BAD architecture are evaluated to quantify the adversarial life cycle for a black box Sentiment Analysis system.
• As a challenging diagnostic, the Jigsaw Toxic Bias dataset is selected as the baseline in our performance tool.