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UPSET and ANGRI : Breaking High Performance Image Classifiers
July 04, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Sayantan Sarkar, Ankan Bansal, Upal Mahbub, Rama Chellappa
arXiv ID
1707.01159
Category
cs.CV: Computer Vision
Citations
112
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In this paper, targeted fooling of high performance image classifiers is achieved by developing two novel attack methods. The first method generates universal perturbations for target classes and the second generates image specific perturbations. Extensive experiments are conducted on MNIST and CIFAR10 datasets to provide insights about the proposed algorithms and show their effectiveness.
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