International Business Machines Corporation

Massachusetts Institute of Technology

Akhilan Boopathy

Pin-Yu Chen

Sijia Liu

Chuang Gan

Luca Daniel

Gaoyuan Zhang

Tsui-Wei Weng

A certification method, system, and computer program product include certifying an adversarial robustness of a convolutional neural network by deriving an analytic solution for a neural network output using an efficient upper bound and an efficient lower bound on an activation function and applying the analytic solution in computing a certified robustness.

Framework for certifying a lower bound on a robustness level of convolutional neural networks

Embodiments relate to a system, program product, and method to support a convolutional neural network (CNN). A class-specific discriminative image region is localized to interpret a prediction of a CNN and to apply a class activation map (CAM) function to received input data. First and second attacks are generated on the CNN with respect to the received input data. The first attack generates first perturbed data and a corresponding first CAM, and the second attack generates second perturbed data and a corresponding second CAM. An interpretability discrepancy is measured to quantify one or more differences between the first CAM and the second CAM. The measured interpretability discrepancy is applied to the CNN. The application is a response to an inconsistency between the first CAM and the second CAM and functions to strengthen the CNN against an adversarial attack.

Interpretability-aware adversarial attack and defense method for deep learnings

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Cambridge, MA, United States of America

Akhilan Boopathy