Methods and systems for encoding images

Patent metadata

Field	Value
Publication number	US-2022237882-A1
Application number	US-201917614929-A
Country	US
Kind code	A1
Filing date	May 28, 2019
Priority date	May 28, 2019
Publication date	Jul 28, 2022
Grant date	—

Abstract

The present disclosure is directed to encoding images. In particular, one or more computing devices can receive data representing one or more machine learning (ML) models configured, at least in part, to encode images comprising objects of a particular type. The computing device(s) can receive data representing an image comprising one or more objects of the particular type. The computing device(s) can generate, based at least in part on the data representing the image and the data representing the ML model(s), data representing an encoded version of the image that alters at least a portion of the image comprising the object(s) such that when the encoded version of the image is decoded, the object(s) are unrecognizable as being of the particular type by one or more object-recognition ML models based at least in part upon which the ML model(s) configured to encode the images were trained.

First claim

What is claimed is:

1 . A computer-implemented method comprising:
receiving, by one or more computing devices, data representing one or more machine learning (ML) models configured, at least in part, to encode images comprising objects of a particular type; receiving, by the one or more computing devices, data representing an image comprising one or more objects of the particular type; and generating, by the one or more computing devices and based at least in part on the data representing the image and the data representing the one or more ML models, data representing an encoded version of the image that alters at least a portion of the image comprising the one or more objects such that when the encoded version of the image is decoded, the one or more objects are unrecognizable as being of the particular type by one or more object-recognition ML models based at least in part upon which the one or more ML models configured to encode the images were trained.

2 . The computer-implemented method of claim 1 , comprising communicating, by the one or more computing devices and to a remotely located computing system, the data representing the encoded version of the image, wherein the remotely located computing system is configured to:
receive the data representing the encoded version of the image; and generate, based at least in part on the data representing the encoded version of the image and one or more ML models trained in conjunction with the one or more ML models configured to encode the images, data representing a decoded version of the image in which the one or more objects are unrecognizable as being of the particular type by the one or more object-recognition ML models.

3 . The computer-implemented method of claim 2 , wherein:
generating the data representing the encoded version of the image comprises generating data representing the encoded version of the image such that one or more objects of a different type from the particular type are recognizable in the decoded version of the image as being of the different type by at least one of the one or more object-recognition ML models; and the remotely located computing system is configured to utilize the at least one of the one or more object-recognition ML models to identify the one or more objects of the different type in the decoded version of the image as being of the different type.

4 . The computer-implemented method of claim 1 , wherein:
the image comprises one or more objects of a different type from the particular type; the one or more ML models configured to encode the images are configured to encode images comprising objects of the different type; and generating the data representing the encoded version of the image comprises generating data representing the encoded version of the image such that the encoded version of the image alters at least a portion of the image comprising the one or more objects of the different type such that when the encoded version of the image is decoded, the one or more objects of the different type are unrecognizable as being of the different type by the one or more object-recognition ML models.

5 . The computer-implemented method of claim 4 , wherein generating the data representing the encoded version of the image comprises utilizing a common ML model of the one or more ML models configured to encode the images to generate data representing the encoded version of the image such that the encoded version of the image alters:
the at least a portion of the image comprising the one or more objects of the particular type such that when the encoded version of the image is decoded, the one or more objects of the particular type are unrecognizable as being of the particular type by the one or more object-recognition ML models; and the at least a portion of the image comprising the one or more objects of the different type such that when the encoded version of the image is decoded, the one or more objects of the different type are unrecognizable as being of the different type by the one or more object-recognition ML models.

6 . The computer-implemented method of claim 4 , wherein generating the data representing the encoded version of the image comprises:
utilizing a first ML model of the one or more ML models configured to encode the images to encode the image to generate data representing a modified version of the image such that the modified version of the image alters the at least a portion of the image comprising the one or more objects of the particular type such that when the modified version of the image is decoded, the one or more objects of the particular type are unrecognizable as being of the particular type by the one or more object-recognition ML models; and utilizing a second ML model of the one or more ML models configured to encode the images to encode a decoded version of the modified version of the image to generate data representing the encoded version of the image such that the encoded version of the image alters the at least a portion of the image comprising the one or more objects of the different type such that when the encoded version of the image is decoded, the one or more objects of the different type are unrecognizable as being of the different type by the one or more object-recognition ML models, the second ML model being different and distinct from the first ML model.

7 . A system comprising:
one or more processors; and a memory storing instructions that when executed by the one or more processors cause the system to perform operations comprising:
receiving, from a remotely located computing device, data representing an encoded version of an image comprising one or more objects of a particular type, the encoded version of the image altering at least a portion of the image comprising the one or more objects such that when the encoded version of the image is decoded, the one or more objects are unrecognizable as being of the particular type by one or more object-recognition machine learning (ML) models;
identifying data representing one or more ML models generated based at least in part on the one or more object-recognition ML models; and
generating, based at least in part on the data representing the encoded version of the image and the data representing the one or more ML models generated based at least in part on the one or more object-recognition ML models, data representing a decoded version of the image in which the one or more objects are unrecognizable as being of the particular type by the one or more object-recognition ML models.

8 . The system of claim 7 , wherein:
generating the data representing the decoded version of the image comprises generating data representing the decoded version of the image such that one or more objects of a different type from the particular type are recognizable in the decoded version of the image as being of the different type by at least one of the one or more object-recognition ML models; and the operations comprise utilizing the at least one of the one or more object-recognition ML models to identify the one or more objects of the different type in the decoded version of the image as being of the different type.

9 . The system of claim 7 , wherein:
the image comprises one or more objects of a different type from the particular type; the data representing the encoded version of the image comprises data that alters at least a portion of the image comprising the one or more objects of the different type such that when the encoded version of the image is decoded, the one or more objects of the different type are unrecognizable as being of the different type by the one or more object-recognition ML models; and the one or more objects of the different

Assignees

• Google Llc

Inventors

• Baluja Shumeet
• Sukthankar Rahul

Classifications

• G06V10/95 Primary

  structured as a network, e.g. client-server architectures · CPC title

• G06F18/2413

  based on distances to training or reference patterns · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/044

  Recurrent networks, e.g. Hopfield networks · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title