Closing the Backdoor in AI Security: Adversarial Robustness Toolbox v0.3.0.

Yesterday we announced a new release of the Adversarial Robustness Toolbox, an open-source software library to support researchers and developers in defending neural networks against adversarial attacks. The new release provides a method for defending against poisoning and "backdoor" attacks in machine learning models. We announced the release at Black Hat USA, the world's leading information security event. Machine learning models are often trained on data from potentially untrustworthy sources, including crowd-sourced information, social media data, and user-generated data such as customer satisfaction ratings, purchasing history, or web traffic [1]. Recent work has shown that adversaries can introduce backdoors or "trojans" in machine learning models by poisoning training sets with malicious samples [2].