Automatically determining whether an activation cluster contains poisonous data

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United States of America Patent

PATENT NO 11487963
APP PUB NO 20210081708A1
SERIAL NO

16571321

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Abstract

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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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Patent Owner(s)

  • INTERNATIONAL BUSINESS MACHINE CORPORATION

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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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