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Denoising Dictionary Learning Against Adversarial Perturbations
[article]
2018
arXiv
pre-print
We propose denoising dictionary learning (DDL), a simple yet effective technique as a protection measure against adversarial perturbations. We examined denoising dictionary learning on MNIST and CIFAR10 perturbed under two different perturbation techniques, fast gradient sign (FGSM) and jacobian saliency maps (JSMA). We evaluated it against five different deep neural networks (DNN) representing the building blocks of most recent architectures indicating a successive progression of model
arXiv:1801.02257v1
fatcat:cspn3zr6erflbjwu5sp6wt7ebe