Title: RoMA: a Method for Neural Network Robustness Measurement and Assessment . Abstract: Neural network models have become the leading solution for various tasks, such
as classification, language processing, protein folding, and others. However, their
reliability is heavily plagued by adversarial inputs: small input perturbations that
cause the model to produce erroneous output, thus impairing the model’s robustness.
Adversarial inputs can occur naturally when the system’s environment behaves
randomly, even in the absence of a malicious adversary, and are thus a severe
cause for concern when attempting to deploy neural networks within critical
systems. In this paper, we present a new statistical method, called Robustness
Measurement and Assessment (RoMA), which can accurately measure the robustness
of a neural network model. Specifically, RoMA determines the probability
that a random input perturbation might cause misclassification. The method allows
us to provide formal guarantees regarding the expected number of errors a
trained model will have after deployment. Our approach can be implemented on
large-scale, black-box neural networks, which is a significant advantage compared
to recently proposed verification methods. We apply our approach in two ways:
comparing the robustness of different models, and measuring how a model’s robustness
is affected by the scale of adversarial perturbation. One interesting insight
obtained through this work is that, in a classification network, different output
labels can exhibit very different robustness levels. We term this phenomenon
Categorial Robustness. Our ability to perform risk and robustness assessments
on a categorial basis opens the door to risk mitigation, which may prove to be a
significant step towards neural network certification in safety-critical applications.