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Deep convolutional neural networks accurately classify a diverse range ofnatural images, but may be easily deceived when designed, imperceptibleperturbations are embedded in the images. In this paper, we design amulti-pronged training, input transformation, and image ensemble system that isattack agnostic and not easily estimated. Our system incorporates two novelfeatures. The first is a transformation layer that computes feature levelpolynomial kernels from class-level training data samples and iterativelyupdates input image copies at inference time based on their feature kerneldifferences to create an ensemble of transformed inputs. The second is aclassification system that incorporates the prediction of the undefendednetwork with a hard vote on the ensemble of filtered images. Our evaluations onthe CIFAR10 dataset show our system improves the robustness of an undefendednetwork against a variety of bounded and unbounded white-box attacks underdifferent distance metrics, while sacrificing little accuracy on clean images.Against adaptive full-knowledge attackers creating end-to-end attacks, oursystem successfully augments the existing robustness of adversarially trainednetworks, for which our methods are most effectively applied.

A Perturbation Resistant Transformation and Classification System for Deep Neural Networks