Nxp B.v.

Brian Ermans

Christine Van Vredendaal

Wilhelmus Petrus Adrianus Johannus Michiels

Gerardus Antonius Franciscus Derks

Joppe Willem Bos

Nikita Veshchikov

Peter Doliwa

Simon Johann Friedberger

Frederik Dirk Schalij

Christiaan Kuipers

Vincent Verneuil

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

A method and machine learning system for detecting adversarial examples is provided. A first machine learning model is trained with a first machine learning training data set having only training data samples with robust features. A second machine learning model is trained with a second machine learning training data set, the second machine learning training data set having only training data samples with non-robust features. A feature is a distinguishing element in a data sample. A robust feature is more resistant to adversarial perturbations than a non-robust feature. A data sample is provided to each of the first and second trained machine learning models during an inference operation. if the first trained machine learning model classifies the data sample with high confidence, and the second trained machine learning model classifies the data sample differently with a high confidence, then the data sample is determined to be an adversarial example.

Method and machine learning system for detecting adversarial examples

A method and data processing system for making a machine learning model more resistant to adversarial examples are provided. In the method, an input for a machine learning model is provided. A randomly generated mask is added to the input to produce a modified input. The modified input is provided to the machine learning model. The randomly generated mask negates the effect of a perturbation added to the input for causing the input to be an adversarial example. The method may be implemented using the data processing system.

Method and data processing system for making machine learning model more resistent to adversarial examples

A method is provided for analyzing a classification in a machine learning model (ML). In the method, the ML model is trained using a training dataset to produce a trained ML model. One or more samples are provided to the trained ML model to produce one or more prediction classifications. A gradient is determined for the one of more samples at a predetermined layer of the trained ML model. The one or more gradients and the one or more prediction classifications for each sample are stored. Also, an intermediate value of the ML model may be stored. Then, a sample is chosen to analyze. A gradient of the sample is determined if the gradient was not already determined when the at least one gradient is determined. Using the at least one gradient, and one or more of a data structure, a predetermined metric, and an intermediate value, the k nearest neighbors to the sample are determined. A report comprising the sample and the k nearest neighbors may be provided for analysis.

Method for analyzing a prediction classification in a machine learning model

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.

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's-Hertogenbosch, Netherlands

Brian Ermans