Nxp B.v.

Frederik Dirk Schalij

Wilhelmus Petrus Adrianus Johannus Michiels

Jan Hoogerbrugge

Brian Ermans

Gerardus Antonius Franciscus Derks

A method is provided for watermarking a machine learning model. A sequence of bits is generated. The sequence of bits may be text characters divided into chunks. A selected plurality of input samples from training data is divided into subsets of input samples. All of the input samples of each subset of the subsets of input samples are labeled with a same first label in a problem domain of the ML model. Each chunk is combined with a subset of the labeled subsets to produce a plurality of labeled trigger samples. Each trigger sample of each set of the plurality of sets is relabeled to have a second label different from the first label and in the problem domain to produce a relabeled set of trigger samples. The ML model is trained with the training data and the relabeled trigger samples to produce a watermarked ML model.

Method for watermarking a machine learning model

A method is provided for watermarking a machine learning model used for object detection. In the method, a first subset of a labeled set of ML training samples is selected. Each of one or more objects in the first subset includes a class label. A pixel pattern is selected to use as a watermark in the first subset of images. The pixel pattern is made partially transparent. A target class label is selected. One or more objects of the first subset of images are relabeled with the target class label. In another embodiment, the class labels are removed from objects in the subset of images instead of relabeling them. Each of the first subset of images is overlaid with the partially transparent and scaled pixel pattern. The ML model is trained with the set of training images and the first subset of images to produce a trained and watermarked ML model.

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 is provided for watermarking a machine learning model used for object detection or image classification. In the method, a first subset of a labeled set of ML training samples is selected. The first subset is of a predetermined class of images. In one embodiment, the first pixel pattern is selected and sized to have substantially the same dimensions as each sample of the first subset or each bounding box in the case of an object detector. Each sample of the first subset is relabeled to have a different label than the original label. An opacity of the pixel pattern may be adjusted independently for different parts of the pattern. The ML model is trained with the labeled set of ML training samples and the first subset of relabeled ML training samples. Using multiple different opacity factors provides both reliability and credibility to the watermark.

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Eindhoven, Netherlands

Frederik Dirk Schalij