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公开(公告)号：US11640646B2
公开(公告)日：2023-05-02
申请号：US17199526
申请日：2021-03-12
Applicant: NXP B.V.
IPC: G06T1/00 , G06F18/214 , G06F18/21 , G06N20/00
Abstract: 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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公开(公告)号：US12013922B2
公开(公告)日：2024-06-18
申请号：US17444108
申请日：2021-07-30
Applicant: NXP B.V.
CPC classification number: G06F21/16 , G06N5/04 , G06N20/00 , G06T3/40 , G06V20/584
Abstract: 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.
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公开(公告)号：US20230029578A1
公开(公告)日：2023-02-02
申请号：US17444108
申请日：2021-07-30
Applicant: NXP B.V.
Abstract: 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.
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公开(公告)号：US20220292623A1
公开(公告)日：2022-09-15
申请号：US17199526
申请日：2021-03-12
Applicant: NXP B.V.
Abstract: 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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Patent Agency Ranking
公开(公告)号：US20220261571A1
公开(公告)日：2022-08-18
申请号：US17176583
申请日：2021-02-16
Applicant: NXP B.V.
Inventor： Gerardus Antonius Franciscus DERKS , Wilhelmus Petrus Adrianus Johannus Michiels , Brian Ermans , Frederik Dirk Schalij
Abstract: 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.
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公开(公告)号：US20250013721A1
公开(公告)日：2025-01-09
申请号：US18347740
申请日：2023-07-06
Applicant: NXP B.V.
IPC: G06F21/16
Abstract: 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.
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公开(公告)号：US11961314B2
公开(公告)日：2024-04-16
申请号：US17176583
申请日：2021-02-16
Applicant: NXP B.V.
Inventor： Gerardus Antonius Franciscus Derks , Wilhelmus Petrus Adrianus Johannus Michiels , Brian Ermans , Frederik Dirk Schalij
IPC: G06V10/20 , G06F18/213 , G06N5/04 , G06N20/00 , G06V20/64
CPC classification number: G06V20/64 , G06F18/213 , G06N5/04 , G06N20/00 , G06V10/255
Abstract: 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.
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公开(公告)号：US11699208B2
公开(公告)日：2023-07-11
申请号：US17199526
申请日：2021-03-12
Applicant: NXP B.V.
IPC: G06T1/00 , G06F18/214 , G06F18/21 , G06N20/00
CPC classification number: G06T1/0021 , G06F18/214 , G06F18/217 , G06N20/00
Abstract: 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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公开(公告)号：US20230040470A1
公开(公告)日：2023-02-09
申请号：US17444682
申请日：2021-08-09
Applicant: NXP B.V.
Inventor： Brian Ermans , Peter Doliwa , Gerardus Antonius Franciscus Derks , Wilhelmus Petrus Adrianus Johannus Michiels , Frederik Dirk Schalij
Abstract: A method is provided for generating a visualization for explaining a behavior of a machine learning (ML) model. In the method, an image is input to the ML model for an inference operation. The input image has an increased resolution compared to an image resolution the ML model was intended to receive as an input. A resolution of a plurality of resolution-independent convolutional layers of the neural network are adjusted because of the increased resolution of the input image. A resolution-independent convolutional layer of the neural network is selected. The selected resolution-independent convolutional layer is used to generate a plurality of activation maps. The plurality of activation maps is used in a visualization method to show what features of the image were important for the ML model to derive an inference conclusion. The method may be implemented in a computer program having instructions executable by a processor.
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