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US 2024/0193265 A1Published application
Method for Protecting an Embedded Machine Learning Model
AssigneeRobert Bosch GmbH
InventorsBenjamin Hettwer, Christoph Schorn
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Abstract
A method for protecting an embedded machine learning model from at least one physical attack includes (i) ascertaining a monitoring input, wherein the monitoring input is based on at least one intermediate result from the machine learning model, (ii) evaluating the ascertained monitoring input by way of a monitoring system, and (iii) detecting the at least one physical attack on the basis of the evaluation.
Claims19
A method for protecting an embedded machine learning model from at least one physical attack, comprising:
- ascertaining a monitoring input, wherein the monitoring input is based on at least one intermediate result from the machine learning model;
- evaluating the ascertained monitoring input by way of a monitoring system;
- and
- detecting the at least one physical attack on the basis of the evaluation,
- wherein the monitoring system comprises a further machine learning model which is configured to perform the evaluation of the ascertained monitoring input, and
- wherein the further machine learning model comprises fewer neurons than the embedded machine learning model being protected.
3 more elements
Dependencies are inferred from the claim text.