METHODS AND SYSTEMS FOR ENGING IMAGES

Description

FIELD

[0001] The present disclosure relates generally to digital imagery. More particularly, the present disclosure relates to encoding images.BACKGROUND

[0002] Computing devices (e.g., desktop computers, laptop computers, tablet computers, smartphones, wearable computing devices, and / or the like) are ubiquitous in modern society. They can support communications between their users, provide their users with information about their environments, current events, the world at large, and / or the like. A popular use of such devices is generating, manipulating, communicating, and / or the like digital imagery (e.g., pictures, video, and / or the like). Mirjalili Vahid et al: "Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images", 2018 IEEE International Conference on Biometrics (ICB), 20 February 2018, pages 82-89, XP033374917 describes a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. US 2018 / 336463 A1 describes systems and methods that implement domain-specific obfuscating of data when processing the data through machine learning (ML), which can secure and preserve privacy of information contained within the data. For instance, various embodiments provide domain-specific techniques for obscuring, and possibly compressing, data.SUMMARY

[0003] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the embodiments.

[0004] One example aspect of the present disclosure is directed to a computer-implemented method as set forth in claim 1. The invention is defined by the set of appended claims.

[0005] Another example aspect of the present disclosure is directed to a system as set forth in claim 7.

[0006] A further example aspect of the present disclosure is directed to one or more non-transitory computer-readable media. The non-transitory computer-readable media can comprise instructions that when executed by one or more computing devices cause the computing device(s) to perform operations of the computer-implemented method.

[0007] Other aspects of the present disclosure are directed to various systems, apparatuses, non-transitory computer-readable media, user interfaces, and electronic devices.

[0008] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following description and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate example embodiments of the present disclosure and, together with the description, serve to explain the related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Detailed discussion of embodiments directed to one of ordinary skill in the art is set forth in the specification, which refers to the appended figures, in which: FIG. 1 depicts an example computing environment according to example embodiments of the present disclosure; FIG. 2 depicts an example event sequence according to example embodiments of the present disclosure; FIG. 3 depicts example pseudo code for generating one or more models according to example embodiments of the present disclosure; and FIG. 4 depicts an example method according to example embodiments of the present disclosure. DETAILED DESCRIPTION

[0010] Example aspects of the present disclosure are directed to encoding images (e.g., static images, animated images, video frames, and / or the like). Specifically, aspects of the present disclosure are directed to generating and utilizing one or more machine learning (ML) models to encode images such that one or more portions of the images that include one or more objects of one or more particular types (e.g., people, specific types of animals, certain types of inanimate objects, and / or the like) are altered (e.g., in order to provide privacy, and / or the like). In accordance with aspects of the disclosure, the ML model(s) configured to encode the images can be generated based at least in part on one or more object-recognition ML models and can be configured to encode the images such that the image portion(s) that include the object(s) of the particular type(s) are altered such that when the images are decoded, the object(s) are unrecognizable as being of the particular type(s) by the object-recognition ML model(s). In some embodiments, the object-recognition ML model(s) can be utilized to generate (e.g., train, and / or the like) the ML model(s) configured to encode the images such that objects recognized in the images by the object-recognition ML model(s) and designated for privacy are not recognized in decoded versions of the images by the object-recognition ML model(s), while other objects recognized in the images by the object-recognition ML model(s) are also recognized in the decoded versions of the images by the object-recognition ML model(s).

[0011] For example, one or more computing devices (e.g., computers, mobile devices, and / or the like) can receive (e.g., from a camera, memory, network interface, and / or the like) data representing an image. The image can include various objects (e.g., a person's face, a dog, the license plate of a car, and / or the like). Based at least in part on the data representing the image and one or more ML models configured to encode images, the computing device(s) can generate data representing an encoded version of the image, encode the image, and / or the like.

[0012] The ML model(s) can be configured to encode the images such that one or more portions of the images that include one or more objects of one or more particular types are altered. For example, the ML model(s) can have been generated based at least in part on data (e.g., associated with one or more privacy settings, and / or the like) indicating that image portions including objects of one or more particular types (e.g., people's faces, car license plates, and / or the like) should be altered for privacy, while image portions including objects of one or more other types (e.g., dogs, and / or the like) need not be altered for privacy, and / or the like. Such data can be based at least in part on, for example, one or more user preferences, device settings, application settings, device locations, jurisdictional regulations, privacy policies, and / or the like.

[0013] The computing device(s) can communicate the data representing the encoded version of the image to a remotely located computing system (e.g., server, and / or the like), which can receive the data. Based at least in part on the data representing the encoded version of the image and one or more ML models configured to decode images, the remotely located computing system can generate data representing a decoded version of the image, decode the image, and / or the like.

[0014] In some embodiments, the remotely located computing system, can utilize one or more object-recognition ML models to identify (e.g., recognize, and / or the like) one or more objects (e.g., the dog, and / or the like) in the decoded version of the image as being of their respective type(s) (e.g., as being a dog, and / or the like). The object-recognition ML model(s) may, however, fail to identify (e.g., recognize, and / or the like) one or more other objects (e.g., the person's face, the license plate of the car, and / or the like) in the decoded version of the image as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like) because the portion(s) of the image including such object(s) have been altered by the encoding and / or decoding such that the object(s) (e.g., the person's face, the license plate of the car, and / or the like) are unrecognizable as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like) by the object-recognition ML model(s). For example, as will be described in greater detail below, the ML model(s) configured to encode the images can have been generated in conjunction with the ML model(s) configured to decode the images, and their generation can have been based at least in part on the object-recognition ML model(s).

[0015] In some embodiments, the remotely located computing system, can utilize one or more ML models (e.g., autoencoder networks, generative adversarial networks (GANs), and / or the like) to reconstruct, in the decoded version of the image, one or more portions of the image comprising one or more of the object(s) (e.g., the person's face, the license plate of the car, and / or the like) such that a human viewing a rendering of the decoded version of the image can identify (e.g., recognize, and / or the like), in the rendering, such object(s) as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like).

[0016] In some embodiments, the ML model(s) configured to encode the images can include a single common (e.g., the same, and / or the like) ML model configured to encode the images such that one or more portions of the images that include one or more of multiple different types of objects (e.g., people's faces, car license plates, and / or the like) are altered. Additionally or alternatively, the ML model(s) configured to encode the images can include multiple different and distinct ML models, and each of such ML model(s) can be configured to encode the images such that one or more portions of the images that include one or more of multiple different types of objects are altered. For example, the ML models can include an ML model configured to encode the images to generate data representing modified versions of the images that alter one or more portions of the images that include objects of a first type (e.g., people's faces, and / or the like), and the ML models can include a different and distinct ML model, which can be configured to encode decoded versions of the modified versions of the images such that one or more portions of the images (e.g., included in the modified versions of the images, and / or the like) that include objects of a second type (e.g., car license plates, and / or the like) are altered.

[0017] As previously indicated, the ML model(s) configured to encode the images can be generated (e.g., trained, and / or the like) in conjunction with the ML model(s) configured to decode the images, and such generation can be based at least in part on the object-recognition ML model(s). For example, one or more computing devices (e.g., associated with the remotely located computing system, and / or the like) can receive data (e.g., training data, and / or the like) representing multiple different and distinct images, and based at least in part on such data and the object-recognition ML model(s), the computing device(s) can generate one or more of the ML model(s) configured to encode the images and / or one or more of the ML model(s) configured to decode the images. Such images can include at least one image comprising one or more objects of the particular type(s) (e.g., people's faces, car license plates, and / or the like) for which image portions including objects of the particular type(s) should be altered (e.g., for privacy, and / or the like).

[0018] In some embodiments, generating one or more of the ML model(s) configured to encode the images and / or one or more of the ML model(s) configured to decode the images can include processing (e.g., training one or more of such ML model(s) based at least in part on, and / or the like) each image of the multiple different and distinct images represented by the received data (e.g., the training data, and / or the like). Processing an image of such multiple different and distinct images can include, for example, generating, based at least in part on the object-recognition ML model(s), data (e.g., one or more multi-dimensional vectors, and / or the like) representing one or more objects identified in the image; generating, based at least in part on the ML model(s) configured to encode the images, data representing an encoded version of the image; and generating, based at least in part on the data representing the encoded version of the image and the ML model(s) configured to decode the images, data representing a decoded version of the image.

[0019] Processing of the image can further include generating, based at least in part on the object-recognition ML model(s), data (e.g., one or more multi-dimensional vectors, and / or the like) representing one or more objects identified in the decoded version of the image; determining a difference (e.g., discrepancy, error, and / or the like) between the data representing the object(s) identified in the image and the data representing the object(s) identified in the decoded version of the image; and modifying one or more of the ML model(s) configured to encode the images and / or one or more of the ML model(s) configured to decode the images such that the modifying reduces the difference between the data representing the object(s) identified in the image and the data representing the object(s) identified in the decoded version of the image, for example, by evaluating a function based at least in part on the difference (e.g., back-propagating the difference through one or more of such ML model(s), utilizing one or more evolutionary algorithms, other suitable ML training paradigms, and / or the like). The difference can be determined at any suitable level (e.g., L1, L2, and / or the like), using any suitable approach (e.g., simple error, absolute value, sum of squares, and / or the like). It will be appreciated that by processing the images in such a manner, the ML model(s) can be configured to minimize, reduce, and / or the like the difference in the objects identified, as opposed to, for example, traditional compression, which seeks to minimize, reduce, and / or the like the pixel-wise difference.

[0020] In some embodiments, for each image of the multiple different and distinct images that includes one or more objects of the particular type(s) (e.g., people's faces, car license plates, and / or the like) for which image portions including objects of the particular type(s) should be altered (e.g., for privacy, and / or the like), prior to determining the difference between the data representing the object(s) identified in the image and the data representing the object(s) identified in the decoded version of the image, the processing of the image can include modifying at least a portion of the data representing the object(s) identified in the image to reflect that the object(s) of the particular type(s) were not identified in the image. It will be appreciated that such modification will shift the target to which the ML model(s) are being trained such that the ML model(s) will be configured to encode / decode the images in a manner that alters the image portions including objects of the particular type(s), for example, because the data associated with such image portions will be eliminated, consolidated, and / or the like due to the fact that the resulting degradation will reduce the difference in the objects identified.

[0021] In some embodiments, for each image of the multiple different and distinct images that includes one or more objects of the particular type(s) (e.g., people's faces, car license plates, and / or the like) for which image portions including objects of the particular type(s) should be altered (e.g., for privacy, and / or the like), modifying the ML model(s) can be based at least in part on a weighted standard error such that the ML model(s) can be modified to encode the images such that altering of one or more portions of the image including the object(s) of the particular type(s) is mitigated. In some of such embodiments, the weighted standard error can mitigate the altering of such portion(s) of the image such that a human viewing a rendering of a decoded version of the image can identify, in the rendering, the object(s) of the particular type(s) as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like).

[0022] In some embodiments, one or more of the ML model(s) described herein can be, otherwise include, and / or the like one or more of various types of ML models, such as neural networks (e.g., deep neural networks, and / or the like), other types of ML models, including non-linear models, linear models, multilayer models, and / or the like. Neural networks can include, for example, feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks, and / or the like), convolutional neural networks, other forms of neural networks, and / or the like. In some embodiments, one or more of the ML model(s) described herein can be trained using supervised techniques, semi-supervised techniques, unsupervised techniques, and / or the like.

[0023] The technology described herein can provide a number of technical effects and benefits. For example, the technology described herein can improve the efficacy of the image encoding, while providing strong privacy (e.g., removing traces that the object(s) of the particular type(s) designated for privacy were ever included in the images, as opposed to merely obscuring recognizable aspects of such object(s), and / or the like). Additionally, the technology described herein can obviate the need (e.g., for a user, and / or the like) to manually identify, label, and / or the like object(s) in an image that should be designated for privacy, and / or the like. Further the technology described herein, can conserve resources (e.g., network bandwidth, and / or the like), for example, because compression associated with encoding an image can reduce the amount of data needed to be transmitted, while simultaneously providing privacy for the designated object(s) in the image, and / or the like.

[0024] With reference now to the figures, example embodiments of the present disclosure will be discussed in further detail.

[0025] FIG. 1 depicts an example computing environment according to example embodiments of the present disclosure.

[0026] Referring to FIG. 1, environment 100 can include one or more computing devices (e.g., one or more desktop computers, laptop computers, tablet computers, mobile devices, smartphones, wearable devices, servers, and / or the like). For example, environment 100 can include computing devices 10, 20, 30, 40, and 50 and computing system 112, which can include one or more computing devices. Environment 100 can also include one or more networks 110 (e.g., one or more wired networks, wireless networks, and / or the like). Network(s) 110 can interface computing device(s) 10, 20, 30, 40, and / or 50 with one another and / or with computing system 112.

[0027] Computing device 10 can include one or more processor(s) 102, one or more communication interfaces 104, and memory 106 (e.g., one or more hardware components for storing executable instructions, data, and / or the like). Communication interface(s) 104 can enable computing device 10 to communicate with computing device(s) 20, 30, 40, and / or 50 and / or with computing system 112. Memory 106 can include (e.g., store, and / or the like) instructions 108. When executed by processor(s) 102, instructions 108 can cause computing device 10 to perform one or more operations, functions, and / or the like described herein. It will be appreciated that computing device(s) 20, 30, 40, and / or 50 can include one or more of the components described above with respect to computing device 10.

[0028] Computing system 112 can include one or more processor(s) 114, one or more communication interfaces 116, and memory 118 (e.g., one or more hardware components for storing executable instructions, data, and / or the like). Communication interface(s) 116 can enable computing system 112 to communicate with computing device(s) 10, 20, 30, 40, and / or 50. Memory 118 can include (e.g., store, and / or the like) instructions 120. When executed by processor(s) 114, instructions 120 can cause computing system 112 to perform one or more operations, functions, and / or the like described herein.

[0029] Unless explicitly indicated otherwise, the operations, functions, and / or the like described herein can be performed by computing device(s) 10, 20, 30, 40, and / or 50 and / or computing system 112 (e.g., by computing device 10, 20, 30, 40, or 50, by computing system 112, by a combination of one or more of computing device(s) 10, 20, 30, 40, and / or 50 and / or computing system 112, and / or the like).

[0030] FIG. 2 depicts an example event sequence according to example embodiments of the present disclosure.

[0031] Referring to FIG. 2,at (202A), computing device 40 can communicate (e.g., via network(s) 110, as indicated by the pattern-filled box over the line extending downward from network(s) 110, and / or the like) data representing multiple different and distinct images (e.g., training data, and / or the like), which can be received by computing system 112. Similarly, at (202B), computing device 50 can communicate data representing multiple different and distinct images (e.g., training data, and / or the like), which can be received by computing system 112. Such images can include at least one image comprising one or more objects of one or more particular types (e.g., people's faces, car license plates, and / or the like) for which image portions including objects of the particular type(s) should be altered (e.g., for privacy, and / or the like).

[0032] As will be described in greater detail below (e.g., with respect to FIGs. 3 and 4, and / or the like), at (204), computing system 112 can utilize the data representing the multiple different and distinct images (e.g., the training data, and / or the like) and one or more object-recognition ML models (e.g., pre-trained models, and / or the like) to generate (e.g., train, and / or the like) one or more ML models configured to encode images comprising objects of the particular type(s) in conjunction with one or more ML models configured to decode such images. The ML model(s) can be configured to encode the images such that one or more portions of the images that include one or more objects of the particular type(s) are altered. For example, the ML model(s) can be generated based at least in part on data (e.g., associated with one or more privacy settings, and / or the like) indicating that image portions including objects of the particular type(s) (e.g., people's faces, car license plates, and / or the like) should be altered for privacy, while image portions including objects of one or more other types (e.g., dogs, and / or the like) need not be altered for privacy, and / or the like. Such data can be based at least in part on, for example, one or more user preferences, device settings, application settings, device locations, jurisdictional regulations, privacy policies, and / or the like.

[0033] At (206), computing system 112 can communicate data representing the ML model(s) configured to encode the images to computing device 10, which can receive the data.

[0034] At (208), computing device 10 can receive (e.g., from a camera, memory, network interface, and / or the like) data representing an image. The image can include various objects (e.g., a person's face, a dog, the license plate of a car, and / or the like).

[0035] At (210), based at least in part on the data representing the image and the ML model(s) configured to encode the images, computing device 10 can generate data representing an encoded version of the image, encode the image, and / or the like. As previously indicated, the ML model(s) can be configured to encode the image such that one or more portions of the image that include one or more objects (e.g., the person's face, the license plate of the car, and / or the like) of the particular type(s) are altered such that when the image is decoded, the object(s) are unrecognizable as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like) by the object-recognition ML model(s).

[0036] In some embodiments, the ML model(s) configured to encode the images can include a single common (e.g., the same, and / or the like) ML model configured to encode the images such that one or more portions of the images that include one or more of multiple different types of objects (e.g., people's faces, car license plates, and / or the like) are altered. Additionally or alternatively, the ML model(s) configured to encode the images can include multiple different and distinct ML models, and each of such ML model(s) can be configured to encode the images such that one or more portions of the images that include one or more of multiple different types of objects are altered. For example, the ML models can include an ML model configured to encode the images to generate data representing modified versions of the images that alter one or more portions of the images that include objects of a first type (e.g., people's faces, and / or the like), and the ML models can include a different and distinct ML model, which can be configured to encode decoded versions of the modified versions of the images such that one or more portions of the images (e.g., included in the modified versions of the images, and / or the like) that include objects of a second type (e.g., car license plates, and / or the like) are altered.

[0037] At (212), computing device 10 can communicate the data representing the encoded version of the image to computing system 112, which can receive the data.

[0038] At (214), computing system 112 can identify the ML model(s) configured to decode the images and can utilize such ML model(s) and the data representing the encoded version of the image to generate data representing a decoded version of the image, decode the image, and / or the like.

[0039] At (216), computing system 112 can utilize one or more of the object-recognition ML model(s) to identify (e.g., recognize, and / or the like) one or more objects (e.g., the dog, and / or the like) in the decoded version of the image as being of their respective type(s) (e.g., as being a dog, and / or the like). The object-recognition ML model(s) may, however, fail to identify (e.g., recognize, and / or the like) one or more other objects (e.g., the person's face, the license plate of the car, and / or the like) in the decoded version of the image as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like) because the portion(s) of the image including such object(s) have been altered by the encoding and / or decoding such that the object(s) (e.g., the person's face, the license plate of the car, and / or the like) are unrecognizable as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like) by the object-recognition ML model(s).

[0040] At (218), computing system 112 can utilize one or more ML models (e.g., autoencoder networks, generative adversarial networks (GANs), and / or the like) to reconstruct, in the decoded version of the image, one or more portions of the image comprising one or more of the object(s) (e.g., the person's face, the license plate of the car, and / or the like) such that a human viewing a rendering of the decoded version of the image can identify (e.g., recognize, and / or the like), in the rendering, such object(s) as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like).

[0041] FIG. 3 depicts example pseudo code for generating one or more models according to example embodiments of the present disclosure, and FIG. 4 depicts an example method according to example embodiments of the present disclosure.

[0042] As previously indicated, the ML model(s) configured to encode the images and / or the ML model(s) configured to decode the images can be generated (e.g., trained, and / or the like) based at least in part on the data representing the multiple different and distinct images (e.g., the training data, and / or the like). For example, each of such multiple different and distinct images can be processed in generating (e.g., training, and / or the like) such ML model(s).

[0043] Referring to FIGs. 3 and 4, such processing can include, for each image (e.g., I, and / or the like) of such images, at (402), one or more computing devices (e.g., computing system 112, and / or the like) can utilize the object-recognition ML model(s) (e.g., pre-trained models, and / or the like) to generate data, for example, one or more multi-dimensional vectors (e.g., V1, V2, ... VN), and / or the like, representing one or more objects identified in the image (e.g., I, and / or the like).

[0044] At (404), if the image (e.g., I, and / or the like) includes one or more objects of the particular type(s) (e.g., people's faces, car license plates, and / or the like) for which image portions including objects of the particular type(s) should be altered, the computing device(s) can modify at least a portion of the data representing the object(s) identified in the image (e.g., V1, V2, ... VN) to reflect that the object(s) of the particular type(s) were not identified in the image.

[0045] At (406), the computing device(s) can utilize the ML model(s) configured to encode the images to generate data representing an encoded version of the image.

[0046] At (408), the computing device(s) can utilize the ML model(s) configured to decode the images and the data representing the encoded version of the image to generate data representing a decoded version of the image (e.g., I', and / or the like).

[0047] At (410), the computing device(s) can utilize the object-recognition ML model(s) to generate data, for example, one or more multi-dimensional vectors (e.g., V'1, V'2, . . . V'N), and / or the like, representing one or more objects identified in the decoded version of the image (e.g., I', and / or the like).

[0048] At (412), the computing device(s) can determine a difference, for example, a discrepancy, error (e.g., (V'1-V1) + (V'2-V2) + . . . (V'N-VN)), and / or the like, between the data representing the object(s) identified in the image and the data representing the object(s) identified in the decoded version of the image.

[0049] At (414), the computing device(s) can modify one or more of the ML model(s) configured to encode the images and / or one or more of the ML model(s) configured to decode the images such that the modifying reduces the difference between the data representing the object(s) identified in the image and the data representing the object(s) identified in the decoded version of the image, for example, by evaluating a function based at least in part on the difference (e.g., back-propagating the difference through one or more of such ML model(s), utilizing one or more evolutionary algorithms, other suitable ML training paradigms, and / or the like). The difference can be determined at any suitable level (e.g., L1, L2, and / or the like), using any suitable approach (e.g., simple error, absolute value, sum of squares, and / or the like). It will be appreciated that by processing the images in such a manner, the ML model(s) can be configured to minimize, reduce, and / or the like the difference in the objects identified, as opposed to, for example, traditional compression, which seeks to minimize, reduce, and / or the like the pixel-wise difference.

[0050] In some embodiments, for each image of the multiple different and distinct images that includes one or more objects of the particular type(s) (e.g., people's faces, car license plates, and / or the like) for which image portions including objects of the particular type(s) should be altered (e.g., for privacy, and / or the like), modifying the ML model(s) can be based at least in part on a weighted standard error such that the ML model(s) can be modified to encode the images such that altering of one or more portions of the image including the object(s) of the particular type(s) is mitigated. In some of such embodiments, the weighted standard error can mitigate the altering of such portion(s) of the image such that a human viewing a rendering of a decoded version of the image can identify, in the rendering, the object(s) of the particular type(s) as being of their respective type(s) (e.g., as being a person's face, car license plate, and / or the like).

[0051] The technology discussed herein refers to servers, databases, software applications, and / or other computer-based systems, as well as actions taken and information sent to and / or from such systems. The inherent flexibility of computer-based systems allows for a great variety of possible configurations, combinations, and / or divisions of tasks and / or functionality between and / or among components. For instance, processes discussed herein can be implemented using a single device or component and / or multiple devices or components working in combination. Databases and / or applications can be implemented on a single system and / or distributed across multiple systems. Distributed components can operate sequentially and / or in parallel.

[0052] Various connections between elements are discussed in the above description. These connections are general and, unless specified otherwise, can be direct and / or indirect, wired and / or wireless. In this respect, the specification is not intended to be limiting.

[0053] The depicted and / or described steps are merely illustrative and can be omitted, combined, and / or performed in an order other than that depicted and / or described; the numbering of depicted steps is merely for ease of reference and does not imply any particular ordering is necessary or preferred.

[0054] The functions and / or steps described herein can be embodied in computer-usable data and / or computer-executable instructions, executed by one or more computers and / or other devices to perform one or more functions described herein. Generally, such data and / or instructions include routines, programs, objects, components, data structures, or the like that perform particular tasks and / or implement particular data types when executed by one or more processors in a computer and / or other data-processing device. The computer-executable instructions can be stored on a computer-readable medium such as a hard disk, optical disk, removable storage media, solid-state memory, read-only memory (ROM), random-access memory (RAM), and / or the like. As will be appreciated, the functionality of such instructions can be combined and / or distributed as desired. In addition, the functionality can be embodied in whole or in part in firmware and / or hardware equivalents, such as integrated circuits, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and / or the like. Particular data structures can be used to more effectively implement one or more aspects of the disclosure, and such data structures are contemplated to be within the scope of computer-executable instructions and / or computer-usable data described herein.

[0055] Although not required, one of ordinary skill in the art will appreciate that various aspects described herein can be embodied as a method, system, apparatus, and / or one or more computer-readable media storing computer-executable instructions. Accordingly, aspects can take the form of an entirely hardware embodiment, an entirely software embodiment, an entirely firmware embodiment, and / or an embodiment combining software, hardware, and / or firmware aspects in any combination.

[0056] As described herein, the various methods and acts can be operative across one or more computing devices and / or networks. The functionality can be distributed in any manner or can be located in a single computing device (e.g., server, client computer, user device, and / or the like).

[0057] Aspects of the disclosure have been described in terms of illustrative embodiments thereof. For example, one of ordinary skill in the art can appreciate that the steps depicted and / or described can be performed in other than the recited order and / or that one or more illustrated steps can be optional and / or combined. Any and all features in the following claims can be combined and / or rearranged in any way possible.

[0058] Features illustrated and / or described as part of one embodiment can be used with another embodiment to yield a further embodiment. Thus, it is intended that the present disclosure cover such alterations, variations, and / or equivalents.

Claims

1. A computer-implemented method comprising: receiving (206), by one or more computing devices (10), data representing an encoding machine learning, ML, model configured to receive an image comprising objects of a particular type designated for privacy as input and to output an encoded image altered by removing, from the image, traces that the one or more objects of the particular type designated for privacy were ever included in the image wherein: the encoding ML model is trained, using a training image dataset and a pre-trained object-recognition model configured to recognize one or more objects of the particular type from input images, to output an encoded training image in which any identified one or more objects of the particular type are altered by removing, from each training image, traces that the one or more objects of the particular type designated for privacy were ever included in the training image such that, when the encoded training image is input to a corresponding decoding ML model configured to output a decoded training image, the pre-trained object-recognition model cannot recognize any altered objects in the corresponding decoded training image as being of the particular type; and the corresponding decoding ML model is trained in conjunction with the encoding ML model, using the training image dataset and the pre-trained object-recognition model and other pre-trained object-recognition models configured for recognizing other objects of one or more other types different to the particular type in input images, to output a decoded version of each encoded training image output from the encoding ML model in which any other objects in the decoded training image are recognized by the other object-recognition ML model(s), but in which the pre-trained object recognition model associated with the particular type cannot recognize any objects of the particular type in the decoded training image; receiving (208), by the one or more computing devices (10), data representing the image comprising one or more objects of the particular type and one or more other objects of other types different to the particular type; and generating (210), by the one or more computing devices (10) and based on inputting the data representing the image to the encoding ML model, data representing an encoded version of the image that alters 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 input to the corresponding decoding ML model, the one or more objects of the particular type are unrecognizable as being of the particular type by the object-recognition ML model upon which the encoding ML model was trained and the one or more other objects are recognizable by the one or more other object-recognition ML models upon which the corresponding decoding ML model was trained; and communicating (212), by the one or more computing devices (10) to a remotely located computing system, the data representing the encoded version of the image for decoding.

2. The computer-implemented method of claim 1, comprising communicating, by the one or more computing devices to the 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 inputting the data representing the encoded version of the image to a decoding ML model trained in conjunction with the encoding ML model, 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 object-recognition ML model used to train the encoding ML model and one or more other objects of the other type are recognizable by the one or more other object-recognition ML models used to train the corresponding decoding ML model.

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 other 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 (112) 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 (212), from a remotely located computing device (10), data representing an encoded version of an image generated according to any one of claims 1 to 6, wherein the encoded version of the image is generated to alter at least a portion of the image comprising one or more objects of a particular type by removing, from the image, traces that the one or more objects of the particular type designated for privacy were ever included in the image; identifying (214) data representing the corresponding decoding machine learning, ML, model generated based at least in part on the one or more object-recognition ML models for decoding the encoded version of the image; and generating (214), based at least in part on inputting the data representing the encoded version of the image tothe corresponding decoding ML model, data representing a decoded version of the image in which 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 one or more other objects are recognizable 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 other 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 operations comprise utilizing one or more ML models to reconstruct, in the decoded version of the image, the at least a portion of the image comprising the one or more objects such that a human viewing a rendering of the decoded version of the image can identify, in the rendering, the one or more objects as being of the particular type.

10. The computer-implemented method of any of claims 1 to 6, further comprising, when training the encoding ML model in conjunction with the decoding ML model: receiving (202A) data representing a plurality of different and distinct images that includes at least one image comprising an object of a particular type; and generating (204, 402-414), based at least in part on the data representing the plurality of different and distinct images and the object-recognition ML model configured for recognizing objects of the particular type in images, an encoding ML model configured to encode images that alter at least a portion of the images comprising one or more objects of the particular type, and generating a decoding ML model configured to decode the encoded images to produce decoded images such that the one or more objects of the particular type are unrecognizable as being of the particular type by the the object-recognition ML models and the one or more other objects are recognizable by the one or more other object-recognition ML models configured to recognize the one or more other objects in images.

11. The computer-implemented method of claim 10, wherein generating the encoding ML model configured to encode the images comprises, for each image of the plurality of different and distinct images: generating, based at least in part on the object-recognition ML model for recognizing objects of the particular type, data representing one or more objects identified in the image; generating, based at least in part on the encoding ML model configured to encode the images, data representing an encoded version of the image; generating, based at least in part on the data representing the encoded version of the image and the decoding ML model configured to decode images, data representing a decoded version of the image; generating, based at least in part on the object-recognition ML model for recognizing objects of the particular type, data representing one or more objects of the particular type identified in the decoded version of the image; determining a difference between the data representing the one or more objects identified in the image and the data representing the one or more objects identified in the decoded version of the image; and modifying the encoding ML model configured to encode the images such that the modifying reduces the difference between the data representing the one or more objects identified in the image and the data representing the one or more objects identified in the decoded version of the image.

12. The computer-implemented method of claim 11, wherein generating the encoding ML model configured to encode the images comprises, for each image of the plurality of different and distinct images that comprises at least one object of the particular type and prior to determining the difference between the data representing the one or more objects identified in the image and the data representing the one or more objects identified in the decoded version of the image, modifying at least a portion of the data representing the one or more objects identified in the image that corresponds to the at least one object of the particular type to reflect that the at least one object of the particular type was not identified in the image; and / or wherein, for each image of the plurality of different and distinct images that comprises at least one object of the particular type, modifying the encoding ML model comprises modifying, based at least in part on a weighted standard error, the encoding ML model to encode the images such that altering of one or more portions of the image comprising the at least one object of the particular type is mitigated.

13. The computer-implemented method of claim 12, wherein the weighted standard error mitigates the altering of the one or more portions of the image comprising the at least one object of the particular type such that a human viewing a rendering of a decoded version of the image can identify, in the rendering, the at least one object of the particular type as being of the particular type.

14. The computer-implemented method of claim 10, wherein generating the encoding ML model configured to encode the images comprises generating the encoding ML model configured to encode the images such that one or more other objects of a different type from the particular type are recognizable in the decoded images as being of the different type by at least one of the one or more other object-recognition ML models configured to recognize one or more objects of the different type.

15. The computer-implemented method of any of claims 10 to 14, wherein generating the encoding ML model configured to encode the images comprises generating the encoding ML model configured to encode the images based at least in part on data indicating: one or more image portions including objects of the particular type should be altered for privacy; and one or more image portions including objects of the different type need not be altered for privacy.