Abstract: A system for authenticating digital contents includes a computing platform having a hardware processor and a memory storing a software code. According to one implementation, the hardware processor executes the software code to receive digital content, identify an image of a person depicted in the digital content, determine an ear shape parameter of the person depicted in the image, determine another biometric parameter of the person depicted in the image, and calculate a ratio of the ear shape parameter of the person depicted in the image to the biometric parameter of the person depicted in the image. The hardware processor is also configured to execute the software code to perform a comparison of the calculated ratio with a predetermined value, and determine whether the person depicted in the image is an authentic depiction of the person based on the comparison of the calculated ratio with the predetermined value.

Abstract: According to one implementation, a system for programmatic generation of media content digests includes a computing platform having a hardware processor and a system memory storing a media content digest software code. The hardware processor executes the media content digest software code to identify a media content for use in generating a content digest, the media content including a timecode of the media content, to access a metadata describing the media content and indexed to the timecode, and to identify one or more constraints for the content digest. In addition, the hardware processor executes the media content digest software code to programmatically extract content segments from the media content using the metadata indexed to the timecode and based on the one or more constraints, and to generate the content digest based on the media content from the content segments.

Abstract: A recommendation engine generates contextually relevant media programs tailored to the specific context of the user. The recommendation engine interfaces with one or more Internet-of-Things (IoT) appliances to determine one or more food items stored within the IoT appliance(s) and/or one or more cooking capabilities associated with the IoT appliance(s). The recommendation engine also gathers profile data that reflects a set of preferences associated with the user. Based on the gathered data, the recommendation engine queries a database of cooking program data associated with various cooking programs. The recommendation engine obtains cooking program data that is contextually relevant to the user and then generates a contextual cooking program that describes how to perform a given recipe. During playback of the contextual cooking program, the recommendation engine can modify the contextual cooking program based on the progress of the user in following the recipe.

Abstract: According to one implementation, a system for automating content annotation includes a computing platform having a hardware processor and a system memory storing an automation training software code. The hardware processor executes the automation training software code to initially train a content annotation engine using labeled content, test the content annotation engine using a first test set of content obtained from a training database, and receive corrections to a first automatically annotated content set resulting from the test. The hardware processor further executes the automation training software code to further train the content annotation engine based on the corrections, determine one or more prioritization criteria for selecting a second test set of content for testing the content annotation engine based on the statistics relating to the first automatically annotated content, and select the second test set of content from the training database based on the prioritization criteria.

Abstract: A system for securing a content processing pipeline includes a computing platform having a hardware processor and a memory storing a software code. The hardware processor executes the software code to insert a synthesized test image configured to activate one or more neurons of a malicious neural network into a content stream, provide the content stream as an input stream to a first processing node of the pipeline, and receive an output stream including a post-processed test image. The hardware processor further executes the software code to compare the post-processed test image in the output with an expected image corresponding to the synthesized test image. and to validate at least one portion of the pipeline as secure when the post-processed test image in the output matches the expected image.

Abstract: Various embodiments of the invention disclosed herein provide techniques for automatically displaying and providing electronic feedback about a 3D production asset. A client device executing a software application receives an asset data bundle associated with the 3D production asset. The client device generates a customized user interface based on at least one aspect of the asset data bundle. The client device displays the 3D production asset via the customized user interface. The client device receives an input associated with the 3D production asset via the customized user interface. The client device causes the input to be transmitted to at least one of a media content server and a production database.

Abstract: According to one implementation, a media content annotation system includes a computing platform having a hardware processor and a system memory storing a software code. The hardware processor executes the software code to receive a first version of media content and a second version of the media content altered with respect to the first version, and to map each of multiple segments of the first version of the media content to a corresponding one segment of the second version of the media content. The software code further aligns each of the segments of the first version of the media content with its corresponding one segment of the second version of the media content, and utilizes metadata associated with each of at least some of the segments of the first version of the media content to annotate its corresponding one segment of the second version of the media content.

Abstract: A system for performing authentication of content based on intrinsic attributes includes a computing platform having a hardware processor and a memory storing a content authentication software code. The hardware processor executes the content authentication software code to receive a content file including digital content and authentication data created based on a baseline version of the digital content, to generate validation data based on the digital content, to compare the validation data to the authentication data, and to identify the digital content as baseline digital content in response to determining that the validation data matches the authentication data based on the comparison. The hardware processor is also configured to execute the content authentication software code to identify the digital content as manipulated digital content in response to determining that the validation data does not match the authentication data based on the comparison.

Abstract: Techniques for machine learning optimization are provided. A video comprising a plurality of segments is received, and a first segment of the plurality of segments is processed with a machine learning (ML) model to generate a plurality of tags, where each of the plurality of tags indicates presence of an element in the first segment. A respective accuracy value is determined for each respective tag of the plurality of tags, where the respective accuracy value is based at least in part on a maturity score for the ML model. The first segment is classified as accurate, based on determining that an aggregate accuracy of tags corresponding to the first segment exceeds a predefined threshold. Upon classifying the first segment as accurate, the first segment is bypassed during a review process.

Abstract: There is provided a system including a computing platform having a hardware processor and a memory, and a metadata extraction and management unit stored in the memory. The hardware processor is configured to execute the metadata extraction and management unit to extract a plurality of metadata types from a media asset sequentially and in accordance with a prioritized order of extraction based on metadata type, aggregate the plurality of metadata types to produce an aggregated metadata describing the media asset, use the aggregated metadata to include at least one database entry in a graphical database, wherein the at least one database entry describes the media asset, display a user interface for a user to view tags of metadata associated with the media asset, and correcting presence of one of the tags of metadata associated with the media asset, in response to an input from the user via the user interface.

Abstract: A device for use in a system for tracking a chain-of-manipulation (CoM) of audio-video (AV) content includes a hardware processor, and a system memory storing a CoM tagging software code and an AV processing application. The hardware processor executes the CoM tagging software code to detect manipulation of the AV content by the AV processing application that modifies at least one of video or audio of the AV content, and to generate, when the manipulation is detected, one or more CoM tag(s) including a device identification tag identifying the device used for modifying the AV content. The hardware processor also executes the CoM tagging software code to insert the CoM tag(s) in each modified segment of the AV content modified by the AV processing application, the inserted CoM tag(s) being human and machine imperceptible when the AV content is played back by a playback device on a display.

Abstract: According to one implementation, a system for automating inferential content annotation includes a computing platform having a hardware processor and a system memory storing a software code including a set of rules trained to annotate content inferentially. The hardware processor executes the software code to utilize one or more feature analyzer(s) to apply labels to features detected in the content, access one or more knowledge base(s) to validate at least one of the applied labels, and to obtain, from the knowledge base(s), descriptive data linked to the validated label(s). The software code then infers, using the set of rules, one or more label(s) for the content based on the validated label(s) and the descriptive data, and outputs tags for annotating the content, where the tags include the validated label(s) and the inferred label(s).

Abstract: According to one implementation, an automated audio mapping system includes a computing platform having a hardware processor and a system memory storing an audio mapping software code including an artificial neural network (ANN) trained to identify multiple different audio content types. The hardware processor is configured to execute the audio mapping software code to receive content including multiple audio tracks, and to identify, without using the ANN, a first music track and a second music track of the multiple audio tracks. The hardware processor is further configured to execute the audio mapping software code to identify, using the ANN, the audio content type of each of the multiple audio tracks except the first music track and the second music track, and to output a mapped content file including the multiple audio tracks each assigned to a respective one predetermined audio channel based on its identified audio content type.

Abstract: A system for assessing authenticity of modified content includes a computing platform having a hardware processor and a memory storing a software code including a neural network trained to assess the authenticity of modified content generated based on baseline digital content and including one or more modifications to the baseline digital content. The hardware processor executes the software code to use the neural network to receive the modified content and to assess the authenticity of each of the one or more modifications to the baseline digital content to produce one or more authenticity assessments corresponding respectively to the one or more modifications to the baseline digital content. The hardware processor is also configured to execute the software code to generate an authenticity evaluation of the modified content based on the one or more authenticity assessments, and to output the authenticity evaluation for rendering on a display.

Abstract: According to one implementation, a quality control (QC) system for annotated content includes a computing platform having a hardware processor and a system memory storing an annotation culling software code. The hardware processor executes the annotation culling software code to receive multiple content sets annotated by an automated content classification engine, and obtain evaluations of the annotations applied by the automated content classification engine to the content sets. The hardware processor further executes the annotation culling software code to identify a sample size of the content sets for automated QC analysis of the annotations applied by the automated content classification engine, and cull the annotations applied by the automated content classification engine based on the evaluations when the number of annotated content sets equals the identified sample size.

Abstract: A content annotation system includes a computing platform having a hardware processor and a memory storing a tagging software code including an artificial neural network (ANN). The hardware processor executes the tagging software code to receive content having a content interval including an image of a generic content feature, encode the image into a latent vector representation of the image using an encoder of the ANN, and use a first decoder of the ANN to generate a first tag describing the generic content feature based on the latent vector representation. When a specific content feature learned by the ANN corresponds to the generic content feature described by the first tag, the tagging software code uses a second decoder of the ANN to generate a second tag uniquely identifying the specific content feature based on the latent vector representation, and tags the content interval with the first and second tags.

Abstract: A content annotation system includes a computing platform having a hardware processor and a memory storing a tagging software code including an artificial neural network (ANN). The hardware processor executes the tagging software code to receive content having a content interval including an image of a generic content feature, encode the image into a latent vector representation of the image using an encoder of the ANN, and use a first decoder of the ANN to generate a first tag describing the generic content feature based on the latent vector representation. When a specific content feature learned by the ANN corresponds to the generic content to feature described by the first tag, the tagging software code uses a second decoder of the ANN to generate a second tag uniquely identifying the specific content feature based on the latent vector representation, and tags the content interval with the first and second tags.

Abstract: A content promotion system includes a computing platform having a hardware processor and a system memory storing a conversational agent software code. The hardware processor executes the conversational agent software code to receive user identification data, obtain user profile data including a content consumption history of a user associated with the user identification data, and identify a first predetermined phrase for use in interacting with the user based on the user profile data. In addition, the conversational agent software code initiates a dialog with the user based on the first predetermined phrase, detects a response or non-response to the dialog, updates the user profile data based on the response or non-response, resulting in updated user profile data, identifies a second predetermined phrase for use in interacting with the user based on the updated user profile data, and continues the dialog with the user based on the second predetermined phrase.

Abstract: A media content tagging system includes a computing platform having a hardware processor, and a system memory storing a tag selector software code configured to receive media content having segments, each segment including multiple content elements each associated with metadata tags having respective pre-computed confidence scores. For each content element, the tag selector software code assigns each of the metadata tags to at least one tag group, determines a confidence score for each tag group based on the pre-computed confidence scores of its assigned metadata tags, discards tag groups having less than a minimum number of assigned metadata tags, and filters the reduced number of tag groups based on the second confidence score to identify a further reduced number of tag groups. The tag selector software code then selects at least one representative tag group for a segment from among the further reduced number of tag groups.

Abstract: A content promotion system includes a computing platform having a hardware processor and a system memory storing a conversational agent software code. The hardware processor executes the conversational agent software code to receive user identification data, obtain user profile data including a content consumption history of a user associated with the user identification data, and identify a first predetermined phrase for use in interacting with the user based on the user profile data. In addition, the conversational agent software code initiates a dialog with the user based on the first predetermined phrase, detects a response or non-response to the dialog, updates the user profile data based on the response or non-response, resulting in updated user profile data, identifies a second predetermined phrase for use in interacting with the user based on the updated user profile data, and continues the dialog with the user based on the second predetermined phrase.