Making Machine Learning models more robust more efficiently
Deploying ML models to the real world is prone to uncover domain coverage issues. Let’s robustify the models by generating unseen data! (more…)
Read more »Attacking Machine Learning with Adversarial Examples
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across differen… Read more