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Universal Adversarial Perturbations: Efficiency on a small image dataset

2022-10-10 11:51:42
Waris Radji (ENSEIRB-MATMECA, UB)
arXiv_CV
       
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.04591

PDF

https://arxiv.org/pdf/2210.04591.pdf

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