ADef: an Iterative Algorithm to Construct Adversarial Deformations
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Author / Producer
Date
2023-05
Publication Type
Conference Paper
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yes
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Abstract
While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood. In the past, image classifiers have been shown to be vulnerable to so-called adversarial attacks, which are created by additively perturbing the correctly classified image. In this paper, we propose the ADef algorithm to construct a different kind of adversarial attack created by iteratively applying small deformations to the image, found through a gradient descent step. We demonstrate our results on MNIST with convolutional neural networks and on ImageNet with Inception-v3 and ResNet-101.
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Publication status
published
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Book title
International Conference on Learning Representations (ICLR 2019)
Journal / series
Volume
9
Pages / Article No.
6991 - 7015
Publisher
Curran
Event
7th International Conference on Learning Representations (ICLR 2019)
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Methods
Software
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Date created
Subject
Adversarial examples; deformations; deep neural networks; computer vision
Organisational unit
09603 - Alaifari, Rima (ehemalig) / Alaifari, Rima (former)
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