Adversarial robustness and attacks in Deep Learning
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Date
2022
Authors
Кузьменко, Дмитро
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Abstract
The theoretical underpinnings for this field involve the notions of robustness and astuteness, local Lipschitzness, r-separability of datasets, robustness-accuracy tradeoff, and L-inf distance. This work will cover all the preliminaries, explain the choice of CIFAR-10 with L-inf metric space and eps=8/255 as a main dataset for the task, make use of already well-known attacks and defenses, introduce new ones, and try different ensembles on the 3 most robust models available on the benchmark – Adversarial Weight Perturbation, Augmentations and weight averaging, and Self-COnsistent Robust Error (SCORE-based model).
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Keywords
Local Lipschitzness, Carlini-Wagner, Self-COnsistent Robust Error, Scale and std hyperparameters in Jitter, магістерська робота