How to Stress-Test Your AI Models Without Collecting New Data#
April 15, 2026 — Brandon RichardWebster, Ph.D. & Emily Veenhuis, Senior R&D Engineers, Kitware
AI models rarely fail in controlled lab environments — they fail in the real world. Field data is the gold standard for evaluating robustness, but it’s expensive to collect across every condition that matters. This webinar shows how synthetic perturbation testing with NRTK complements field data — helping you identify where the model is most fragile, where to focus development efforts, and where to add mitigation strategies.
In the accompanying notebook, we:
Install the NRTK package.
Show perturbations on sample imagery to simulate real-world conditions.
Set up T&E analysis through MAITE.
Run controlled parameter sweeps to stress-test model performance.
Perform a light robustness evaluation.
Walk through the full end-to-end workflow presented in the webinar: image perturbation, MAITE-driven evaluation, and parameter sweeps — applied to an aerial object-detection scenario.