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Verity supports the contextual targeting categories defined in the Interactive Advertising Bureau (IAB) Content Taxonomy v1.0, 2.0, and 23.0.
Media Ratings Council (MRC) Content-Level Accreditation
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Supply-side and demand-side platforms (SSPs and DSPs) can implement the Verity service on their own technology platforms, ad exchanges, and ad servers. There are two primary product use cases:
Increased Brand Safety — Publishers can use Verity to identify and assess potentially objectionable digital content prior to publication. Advertisers can deploy Verity to detect objectionable content and avoid serving their advertising messaging adjacent to or embedded within that content.
Optimum Contextual Targeting — Publishers and advertisers can access the Verity service to locate content that is highly relevant to advertisers, enabling contextually appropriate advertising messages to be served with that content.
Verity’s core technology remains unchanged for each implementation. Integrations are accomplished via the Verity API.
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The Verity page analysis process involves the following core components:
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Verity API Gateway: The Verity API Gateway receives a page URL request, authenticates the client request and passes the URL to the Verity API.
Verity API: The Verity API initiates the request and then orchestrates the Content Extractor, Text and Image analyses systems to extract the page data and perform the analyses.
Content Extractor: The Content Extractor accepts page requests sent by the Verity API from a queue. The Content Extractor loads the page URL, downloads the page title, metadata, and HTML and saves it as a text string in the database. If a prominent image is identified for the page, the Content Extractor downloads and saves the image to the database with identification information for the associated page. The Content Extractor passes the Page URL and image information on for text and image analysis.
Text Analysis: The Text Analysis engine applies Natural Language Processing (NLP) for text classification (e.g. IAB and Threat categories) and information extraction (e.g. Keywords).
Image analysis: The Image Analysis engine houses GumGum’s core Computer Vision capabilities in a modular architecture. The Image Analysis component passes images through multiple data models to determine their classification information.
Verity Report: The Verity API retrieves the text and image classification results, applies weighting and merging logic to the results, and returns the final Verity page report to the client.
Video Analysis
Verity analyzes videos for the purposes of content-level contextual targeting and brand safety.
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The Verity video analysis process involves the following core components:
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Verity API Gateway: The Verity API Gateway receives a video URL request, authenticates the client request and passes the URL to the Verity API.
Verity API: The Verity API passes the request to the Video Service to orchestrate video analysis.
Video Service: the Video Service downloads video and audio into separate files.
Audio Transcribe: The audio file is sent for transcription.
Optical Character Recognition (OCR): Verity API verifies if the audio transcription results contain a sufficient sample of at least 50 words. If not, Verity API initiates an OCR job to detect text in the video file and convert the detected text into machine-readable text.
Prism Video Frame Threat Classifier: Video is sent to the Video Threat Classifier for brand safety analysis of video frames.
Verity Text Processing: Verity API passes concatenated text results (comprising transcription, OCR if available, Client metadata title and description) to Verity Tapas Text Processing. The Text Processing engine processes the video transcription, OCR, client metadata title and description by applying Natural Language Processing (NLP) for text classification (e.g. IAB Content Categories v2.0 and Threat categories) and information extraction (e.g. Keywords).
Verity Report: The Verity API accepts the text analysis results, applies result weighting and merging logic, then returns the final video analysis Verity Report to the client.
Brand Safety
Verity Machine learning predicts threat categories by applying data models trained on collections of various kinds of threatening content. Verity’s sophisticated Computer Vision machine learning can identify threatening scenes, such as natural disasters or accidents. Object detection picks out potentially threatening objects within an image, such as weapons, exposed skin or drinks.
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