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Defense against adversarial attacks on deep convolutional neural networks through nonlocal denoising. (arXiv:2206.12685v1 [cs.CV])
June 28, 2022, 1:20 a.m. | Sandhya Aneja, Nagender Aneja, Pg Emeroylariffion Abas, Abdul Ghani Naim
cs.CR updates on arXiv.org arxiv.org
Despite substantial advances in network architecture performance, the
susceptibility of adversarial attacks makes deep learning challenging to
implement in safety-critical applications. This paper proposes a data-centric
approach to addressing this problem. A nonlocal denoising method with different
luminance values has been used to generate adversarial examples from the
Modified National Institute of Standards and Technology database (MNIST) and
Canadian Institute for Advanced Research (CIFAR-10) data sets. Under
perturbation, the method provided absolute accuracy improvements of up to 9.3%
in the …
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