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Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz
Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz
Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment
Adversarial training has proven to be one of the most successful ways to defend models against adversarial examples. This process consists of training a model with an adversarial example to improve the robustness of the model. In this experiment, Torchattacks, a Pytorch library made for importing adversarial examples more easily, was used to determine which attack was the strongest. Later on, the strongest attack was used to train the model and make it more robust against adversarial examples. The datasets used to perform the experiments were MNIST and CIFAR-10. Both datasets were put to the test using PGD, FGSM, and …