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US Patent 11188789 Detecting poisoning attacks on neural networks by activation clustering

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Patent
Patent
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Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
0
Patent Number
111887890
Patent Inventor Names
Jialong Zhang0
Wilka Carvalho0
Benjamin J. Edwards0
Bryant Chen0
Heiko H. Ludwig0
Ian Michael Molloy0
Taesung Lee0
Date of Patent
November 30, 2021
0
Patent Application Number
160577060
Date Filed
August 7, 2018
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Patent Citations Received
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US Patent 11361100 Systems and methods for detecting potentially malicious content in decentralized machine-learning model updates
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US Patent 11538236 Detecting backdoor attacks using exclusionary reclassification
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US Patent 11593641 Automatic generation of synthetic samples using dynamic deep autoencoders
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US Patent 11645515 Automatically determining poisonous attacks on neural networks
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US Patent 11487963 Automatically determining whether an activation cluster contains poisonous data
Patent Primary Examiner
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Stanley K. Hill
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Patent abstract

One embodiment provides a method comprising receiving a training set comprising a plurality of data points, where a neural network is trained as a classifier based on the training set. The method further comprises, for each data point of the training set, classifying the data point with one of a plurality of classification labels using the trained neural network, and recording neuronal activations of a portion of the trained neural network in response to the data point. The method further comprises, for each classification label that a portion of the training set has been classified with, clustering a portion of all recorded neuronal activations that are in response to the portion of the training set, and detecting one or more poisonous data points in the portion of the training set based on the clustering.

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