Poisoning Attacks and Subpopulation Susceptibility


We study black-box attacks on machine learning classifiers where each query to the model incurs some cost or risk of detection to the adversary. We focus explicitly on minimizing the number of queries as a major objective. Specifically, we consider the problem of attacking machine learning classifiers subject to a budget of feature modification cost while minimizing the number of queries, where each query returns only a class and confidence score. We describe an approach that uses Bayesian optimization to minimize the number of queries, and find that the number of queries can be reduced to approximately one tenth of the number needed through a random strategy for scenarios where the feature modification cost budget is low.

In N(eur)IPS Machine Learning and Computer Security Workshop, 2017
Fnu Suya
Fnu Suya
MC2 Postdoctoral Fellow

My research interests include machine learning for security and trustworthy machine learning.