Nxp B.v.

Gerardus Antonius Franciscus Derks

Wilhelmus Petrus Adrianus Johannus Michiels

Brian Ermans

Nikita Veshchikov

Christine Van Vredendaal

Marc Vauclair

Frederik Dirk Schalij

A method is described for analyzing an output of an object detector for a selected object of interest in an image. The object of interest in a first image is selected. A user of the object detector draws a bounding box around the object of interest. A first inference operation is run on the first image using the object detector, and in response, the object detect provides a plurality of proposals. A non-max suppression (NMS) algorithm is run on the plurality of proposals, including the proposal having the object of interest. A classifier and bounding box regressor are run on each proposal of the plurality of proposals and results are outputted. The outputted results are then analyzed. The method can provide insight into why an object detector returns the results that it does.

Method for analyzing an output of an object detector

Various embodiments relate to a method of producing a machine learning model with a fingerprint that maps an input value to an output label, including: selecting a set of extra input values, wherein the set of extra input values does not intersect with a set of training labeled input values for the machine learning model; selecting a first set of artificially encoded output label values corresponding to each of the extra input values in the set of extra input values, wherein the first set of artificially encoded output label values are selected to indicate the fingerprint of a first machine learning model; and training the machine learning model using a combination of the extra input values with associated first set of artificially encoded output values and the set of training labeled input values to produce the first learning model with the fingerprint.

Adding a fingerprint to a machine learning model

A method is provided for protecting a machine learning ensemble. In the method, a plurality of machine learning models is combined to form a machine learning ensemble. A plurality of data elements for training the machine learning ensemble is provided. The machine learning ensemble is trained using the plurality of data elements to produce a trained machine learning ensemble. During an inference operating phase, an input is received by the machine learning ensemble. A piecewise function is used to pseudo-randomly choose one of the plurality of machine learning models to provide an output in response to the input. The use of a piecewise function hides which machine learning model provided the output, making the machine learning ensemble more difficult to copy.

Method for protecting a machine learning ensemble from copying

A method for protecting a machine learning (ML) model is provided. During inference operation of the ML model, a plurality of input samples is provided to the ML model. A distribution of a plurality of output predictions from a predetermined node in the ML model is measured. If the distribution of the plurality of output predictions indicates correct output category prediction with low confidence, then the machine learning model is slowed to reduce a prediction rate of subsequent output predictions. If the distribution of the plurality of categories indicates correct output category prediction with a high confidence, then the machine learning model is not slowed to reduce the prediction rate of subsequent output predictions of the machine learning model. A moving average of the distribution may be used to determine the speed reduction. This makes a cloning attack on the ML model take longer with minimal impact to a legitimate user.

Method and system for protecting a machine learning model against extraction

A method for protecting a machine learning model from copying is provided. The method includes providing a neural network architecture having an input layer, a plurality of hidden layers, and an output layer. Each of the plurality of hidden layers has a plurality of nodes. A neural network application is provided to run on the neural network architecture. First and second types of activation functions are provided. Activation functions including a combination of the first and second types of activation functions are provided to the plurality of nodes of the plurality of hidden layers. The neural network application is trained with a training set to generate a machine learning model. Using the combination of first and second types of activation functions makes it more difficult for an attacker to copy the machine learning model. Also, the neural network application may be implemented in hardware to prevent easy illegitimate upgrading of the neural network application.

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Dongen, Netherlands

Gerardus Antonius Franciscus Derks