Royal Bank of Canada

Ga Wu

Weiguang Ding

Sayedmasoud Hashemi Amroabadi

Christopher Côté Srinivasa

Giuseppe Marcello Antonio Castiglione

Maksims Volkovs

Himanshu Rai

A system and method for adversarial vulnerability testing of machine learning models is proposed that receives as an input, a representation of a non-differentiable machine learning model, transforms the input model into a smoothed model and conducts an adversarial search against the smoothed model to generate an output data value representative of a potential vulnerability to adversarial examples. Variant embodiments are also proposed, directed to noise injection, hyperparameter control, and exhaustive/sampling-based searches in an effort to balance computational efficiency and accuracy in practical implementation. Flagged vulnerabilities can be used to have models re-validated, re-trained, or removed from use due to an increased cybersecurity risk profile.

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Toronto, Canada

Ga Wu