Although neural networks perform very well on the image classification task, they are still vulnerable to adversarial perturbations that can fool a neural network without visibly changing an input image. A paper has shown the existence of Universal Adversarial Perturbations which when added to any image will fool the neural network with a very high probability. In this paper we will try to reproduce the experience of the Universal Adversarial Perturbations paper, but on a smaller neural network architecture and training set, in order to be able to study the efficiency of the computed perturbation.
@article{arxiv.2210.04591,
title = {Universal Adversarial Perturbations: Efficiency on a small image dataset},
author = {Waris Radji},
journal= {arXiv preprint arXiv:2210.04591},
year = {2022}
}