“ART provides tools that enable developers and researchers to defend and evaluate their ML models and applications against a number of adversarial threats, such as evasion, poisoning, extraction, and inference.”

The first two attacks, evasion & poisoning highlight the incredible importance of having high quality data when training models. Evasion is false-negatives that are allowed because the model did not have a diverse enough selection of training data and poisoning can occur when the data sources are not well vetted. Data quality is probably the single biggest problem with ML models, and I wish we’d see more of a focus on it.