Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that protect analytics for resources of a publisher from traffic directed to such resources by malicious entities. An analytics server receives a first message that includes an encrypted token and analytics data for a publisher-provided resource. The token includes a portion of the analytics data and a trust score indicating a likelihood that activity on the resource is attributed to a human (rather than an automated process). The analytics server decrypts the token. The analytics server determines a trustworthiness measure for the analytics data included in the first message based on the trust score (in the decrypted token) and a comparison of the analytics data in the first message and the portion of the analytics data (in the decrypted token). Based on the measure of trustworthiness, the analytics server performs analytics operations using the analytics data.

Abstract: Multiple image verification challenges can be used to identify the location of an object within an initial image. For instance, a first set of tiles is generated using the initial image. This first set is provided to a client computing device for display in a first verification challenge requesting that the user select tiles including the object. In response, a user selection of tiles of the first set is received. These selected tiles are then used to generate a second set of tiles corresponding to a sub-portion of the initial image. The second set of tiles is provided to a client computing device for display in a second verification challenge. In response to the second verification challenge, a user selection of tiles of the second set is received. This user selection of tiles of the second set is then used to determine a location of the object in the image.

Abstract: Multiple image verification challenges can be used to identify the location of an object within an initial image. For instance, a first set of tiles is generated using the initial image. This first set is provided to a client computing device for display in a first verification challenge requesting that the user select tiles including the object. In response, a user selection of tiles of the first set is received. These selected tiles are then used to generate a second set of tiles corresponding to a sub-portion of the initial image. The second set of tiles is provided to a client computing device for display in a second verification challenge. In response to the second verification challenge, a user selection of tiles of the second set is received. This user selection of tiles of the second set is then used to determine a location of the object in the image.

Abstract: Systems and methods for screening unauthorized devices are provided. More particularly, a challenge that includes a first set of data can be generated. The challenge can require a browser of a requesting device to perform a browser task with respect to the first set of data to generate a second set of data. The challenge can be provided to the requesting device and a response to the challenge can be received. The response can include the second set of data generated by the browser of the requesting device through performance of the browser task. It can be determined whether the browser of the requesting device is an authorized browser based, at least in part, on the second set of data. Whether or not the requesting device is authorized to access a resource can be based, at least in part, on whether the browser is an authorized browser.

Abstract: Systems and methods of verifying a user are provided. In particular, a request to engage in a verification process to gain access to an online resource can be received. The request can be provided by a first user device associated with a user. A validation request associated with a second user device associated with the user can be received. The validation request can include a device profile associated with the second user device. It can then be determined whether to validate the second user device based at least in part on the device profile. When it is determined to validate the second user device, the first user device can be granted access to the online resource.

Abstract: Systems and methods of determining image characteristics are provided. More particularly, a first image having an unknown characteristic can be obtained. The first image can be provided to a plurality of user devices in a verification challenge. The verification challenge can include one or more instructions to be presented to a user of each user device. The instructions being determined based at least in part on the first image. User responses can be received, and an unknown characteristic of the first image can be determined based at least in part on the received responses. Subsequent to determining the unknown characteristic of the first image, one or more machine learning models can be trained based at least in part on the determined characteristic.

Abstract: Multiple image verification challenges can be used to identify the location of an object within an initial image. For instance, a first set of tiles is generated using the initial image. This first set is provided to a client computing device for display in a first verification challenge requesting that the user select tiles including the object. In response, a user selection of tiles of the first set is received. These selected tiles are then used to generate a second set of tiles corresponding to a sub-portion of the initial image. The second set of tiles is provided to a client computing device for display in a second verification challenge. In response to the second verification challenge, a user selection of tiles of the second set is received. This user selection of tiles of the second set is then used to determine a location of the object in the image.

Abstract: Systems and methods of determining image characteristics are provided. More particularly, a first image having an unknown characteristic can be obtained. The first image can be provided to a plurality of user devices in a verification challenge. The verification challenge can include one or more instructions to be presented to a user of each user device. The instructions being determined based at least in part on the first image. User responses can be received, and an unknown characteristic of the first image can be determined based at least in part on the received responses. Subsequent to determining the unknown characteristic of the first image, one or more machine learning models can be trained based at least in part on the determined characteristic.

Abstract: Systems and methods of determining image characteristics are provided. More particularly, a first image having an unknown characteristic can be obtained. The first image can be provided to a plurality of user devices in a verification challenge. The verification challenge can include one or more instructions to be presented to a user of each user device. The instructions being determined based at least in part on the first image. User responses can be received, and an unknown characteristic of the first image can be determined based at least in part on the received responses. Subsequent to determining the unknown characteristic of the first image, one or more machine learning models can be trained based at least in part on the determined characteristic.

Abstract: Systems and methods of verifying a user are provided. In particular, a request to engage in a verification process to gain access to an online resource can be received. The request can be provided by a first user device associated with a user. A validation request associated with a second user device associated with the user can be received. The validation request can include a device profile associated with the second user device. It can then be determined whether to validate the second user device based at least in part on the device profile. When it is determined to validate the second user device, the first user device can be granted access to the online resource.

Abstract: Systems and methods for screening unauthorized devices are provided. More particularly, a challenge that includes a first set of data can be generated. The challenge can require a browser of a requesting device to perform a browser task with respect to the first set of data to generate a second set of data. The challenge can be provided to the requesting device and a response to the challenge can be received. The response can include the second set of data generated by the browser of the requesting device through performance of the browser task. It can be determined whether the browser of the requesting device is an authorized browser based, at least in part, on the second set of data. Whether or not the requesting device is authorized to access a resource can be based, at least in part, on whether the browser is an authorized browser.

Abstract: Systems and methods of determining image characteristics are provided. More particularly, a first image having an unknown characteristic can be obtained. The first image can be provided to a plurality of user devices in a verification challenge. The verification challenge can include one or more instructions to be presented to a user of each user device. The instructions being determined based at least in part on the first image. User responses can be received, and an unknown characteristic of the first image can be determined based at least in part on the received responses. Subsequent to determining the unknown characteristic of the first image, one or more machine learning models can be trained based at least in part on the determined characteristic.