Universal Adversarial Perturbations: Efficiency on a small image dataset

Waris Radji

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arXiv · 2210.04591

Universal Adversarial Perturbations: Efficiency on a small image dataset

Waris Radji

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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.

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BibTeX RIS

Waris Radji. 2022-10-10. Universal Adversarial Perturbations: Efficiency on a small image dataset. https://arxiv.org/abs/2210.04591

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