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US Patent 11853436 Protecting cognitive systems from model stealing attacks

OverviewStructured DataIssuesContributors

Contents

Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
0
Patent Number
118534360
Patent Inventor Names
Taesung Lee0
Dong Su0
Ian M. Molloy0
Date of Patent
December 26, 2023
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Patent Application Number
172313690
Date Filed
April 15, 2021
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Patent Citations
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US Patent 9390370 Training deep neural network acoustic models using distributed hessian-free optimization
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US Patent 10657259 Protecting cognitive systems from gradient based attacks through the use of deceiving gradients
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US Patent 10642846 Using a generative adversarial network for query-keyword matching
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US Patent 10733292 Defending against model inversion attacks on neural networks
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US Patent 7409372 Neural network trained with spatial errors
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US Patent 8275803 System and method for providing answers to questions
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Patent Primary Examiner
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Hal Schnee
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CPC Code
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G06N 3/082
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G06N 3/08
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G06N 3/04
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Patent abstract

Mechanisms are provided for obfuscating training of trained cognitive model logic. The mechanisms receive input data for classification into one or more classes in a plurality of predefined classes as part of a cognitive operation of the cognitive system. The input data is processed by applying a trained cognitive model to the input data to generate an output vector having values for each of the plurality of predefined classes. A perturbation insertion engine modifies the output vector by inserting a perturbation in a function associated with generating the output vector, to thereby generate a modified output vector. The modified output vector is then output. The perturbation modifies the one or more values to obfuscate the trained configuration of the trained cognitive model logic while maintaining accuracy of classification of the input data.

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