Accuracy metrics fail to predict performance on long-tail real-world edge cases, both naturally occurring and adversarial. Empirically score and track model robustness for a more complete picture of model performance.
Identify at-risk model classes that are the most likely to fail and to which classes such failures are most likely to fail towards, or as we like to call it, 'failure bias', allowing reduction and shaping risk away from key classes.
Identify noisy labels along with the most efficient adversarial samples that can be included in your training process to improve both accuracy and robustness metrics for better, more secure AI.
Supply chains, insider threats and data breaches put data at risk. Audit your training data with our second-generation approach to identify embedded Trojan attacks, both known and unknown. Coming Q4 2021.
Protect deployed models from model evasion attacks both in batch and real-time. Coming Q1 2022.
Robustness and Security is necessary to achieve Trustworthy AI and it starts with understanding robustness. Deploy models with confidence, protect your brand and the global pace of AI innovation.
14 King Street, Suite 102
Saint John, NB
Phone: (+506) 333-7207