Automatically determining whether an activation cluster contains poisonous data
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United States of America Patent
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Nov 1, 2022
Grant Date -
Mar 18, 2021
app pub date -
Sep 16, 2019
filing date -
Sep 16, 2019
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Abstract
Embodiments relate to a system, program product, and method for automatically determining which activation data points in a neural model have been poisoned to erroneously indicate association with a particular label or labels. A neural network is trained network using potentially poisoned training data. Each of the training data points is classified using the network to retain the activations of the last hidden layer, and segment those activations by the label of corresponding training data. Clustering is applied to the retained activations of each segment, and a cluster assessment is conducted for each cluster associated with each label to distinguish clusters with potentially poisoned activations from clusters populated with legitimate activations. The assessment includes analyzing, for each cluster, a distance of a median of the activations therein to medians of the activations in the labels.
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- INTERNATIONAL BUSINESS MACHINE CORPORATION
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- [Patents Count]
Inventor(s)
Inventor Name | Address | # of filed Patents | Total Citations |
---|---|---|---|
Angel, Nathalie Baracaldo | San Jose, US | 6 | 18 |
Chen, Bryant | San Jose, US | 11 | 77 |
Ludwig, Heiko H | San Francisco, US | 43 | 253 |
Srivastava, Biplav | Rye, US | 98 | 1354 |
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