Abstract: The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.

Abstract: The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.

Abstract: In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.

Abstract: The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.

Abstract: The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.

Abstract: The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.

Abstract: The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.

Abstract: In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.

Abstract: The technology described herein is directed to systems, methods, and software for indexing video. In an implementation, a method comprises identifying one or more regions of interest around target content in a frame of the video. Further, the method includes identifying, in a portion of the frame outside a region of interest, potentially empty regions adjacent to the region of interest. The method continues with identifying at least one empty region of the potentially empty regions that satisfies one or more criteria and classifying at least the one empty region as a negative sample of the target content. In some implementations, the negative sample of the target content in a set of negative samples of the target content, with which to train a machine learning model employed to identify instances of the target content.

Abstract: The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.

Abstract: In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.

Abstract: There is disclosed a technique for use in authentication. The technique comprises receiving an authentication request. The technique also comprises evaluating an attribute associated with the implementation of the authentication request. The attribute is evaluated with respect to at least one available authentication method. The technique also comprises selecting an available authentication method suitable for use in authentication based on the said evaluation.

Abstract: In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.

Abstract: There are disclosed techniques for use in authentication. In one embodiment, the techniques comprise generating first and second distributions. The first distribution relating to risk scores expected to be produced by an authentication system in connection with requests to access a computerized resource. The expected risk scores are based on a normalization process configured to produce risk scores by normalizing raw risk scores in connection with requests. The second distribution relates to risk scores actually produced by the authentication system in connection with requests. The actual risk scores include risk scores normalized by the normalization process. The techniques also comprise comparing the first and second distributions by determining a Kolmogorov-Smirnov distance between the respective distributions. The techniques also comprise initiating, based on the comparison, a failover of the normalization process to a new normalization process for use by the authentication system.

Abstract: There is disclosed a technique for use in authentication. In one embodiment, the technique comprises receiving behavioral information associated with a user. The technique also comprises performing an analysis based on the behavioral information. The technique further comprises determining whether to authenticate the user based on the analysis.

Abstract: Methods and apparatus are provided for configuring the granularity of key performance indicators that are available from a monitored component. A granularity of a key performance indicator provided by a monitored component can be configured by creating a plurality of instances of a statistics collector object, wherein each instance of the statistics collector object collects information for a different context of the key performance indicator; updating an indicator value for a corresponding instance of the statistics collector object when an activity occurs for the context of the key performance indicator; and providing the indicator value for a given context of the key performance indicator to a monitoring object. The configurable granularity can be specified by the product, for example, using an application programming interface.

Abstract: A technique detects mobile device emulation. The technique involves identifying, by processing circuitry, a user apparatus for mobile device emulation detection. The technique further involves collecting, by the processing circuitry, motion sensor data from the identified user apparatus (e.g., samples of accelerometer attributes, gyroscopic attributes, gravity attributes, etc. over multiple time periods). The technique further involves performing, by the processing circuitry, a motion sensor data analysis operation based on the collected motion sensor data. A result of the motion sensor data analysis operation indicates whether the identified user apparatus is a physical mobile device or an emulated mobile device.

Abstract: There is disclosed a technique for use in authentication. The technique comprises selecting an authentication challenge when an authentication request is deemed risky. An attack threat determined to be associated with the authentication request acts as a decision factor in the selection of the authentication challenge such that a biometric challenge is selected in the event that the attack threat describes a stolen device and an out-of-band challenge is selected in the event that the attack threat describes a stolen identification credential.

Abstract: A technique of performing knowledge based authentication (KBA) involves collecting activity data and time data based on operation of a mobile device, the activity data identifying an activity performed by the mobile device, and the time data identifying a time of the activity. The technique further involves generating a KBA question based on the activity data and the time data. The technique further involves providing the KBA question to a user to authenticate the user. Such a technique is capable of being performed by a remote server which communicates with the mobile device over time (e.g., in response to certain events, periodically, combinations thereof, etc.). Accordingly, the remote server is able to accumulate activity and time data which is not stored permanently (or perhaps even at all) by the mobile device, and then generate KBA questions based on that data.

Abstract: A technique performs user authentication. The technique involves generating a first usage pattern from a first set of mobile device communications performed during a first period of time. The technique further involves generating a second usage pattern from a second set of mobile device communications performed during a second period of time. The technique further involves performing a comparison between the first and second usage patterns to determine whether a human provider of the first usage pattern and a human provider of the second usage pattern are the same person. Accordingly, a malicious person who steals a mobile device or operates a mock mobile device would nevertheless be identified as different from the legitimate user because the malicious person does not behave the same way as the legitimate user (e.g., does not have the same phone call behavior, does not have the same text messaging behavior, etc.).