SparseFool
Geometry-inspired sparse attack on deep networks
Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data. Although most attacks usually change values of many image’s pixels, it has been shown that deep networks are also vulnerable to sparse alterations of the input. SparseFool implements an efficient algorithm to compute and control sparse alterations.
inactive
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entered showcase: 2019-09-04
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entry updated: 2024-03-21
This project has not yet been evaluated by the C4DT Factory team.
We will be happy to evaluate it upon request.
Application
Python
Apache-2.0