Channel Context Copyright Detection via Feature Extraction
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Solution Overview
Problem
Current methods for detecting video copyright misuse are inefficient, relying heavily on manual policing or requiring access to original content for watermarking and fingerprinting, which are not scalable or reliable, especially for short videos and large datasets.
Innovation Solution
A method using a feature-based classifier trained on channel features and historical piracy data to predict the likelihood of copyright violations without analyzing the video or audio content, employing machine learning models to identify potential piracy sources based on metadata and user engagement metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video fingerprinting techniques are used to detect copyright violations, then detection accuracy is improved, but storage requirements and processing intensity increase significantly
Solution Approach 1:
The patent extracts only the essential channel features (metadata, user engagement metrics, video properties) from the complete video content, storing and analyzing only these extracted features rather than the entire video database. This reduces storage requirements while maintaining detection capability through the trained classifier model.
Solution Approach 2:
The system performs preliminary extraction and storage of channel features and historical piracy data before actual detection occurs. The classifier is pre-trained on this extracted data, enabling rapid detection without needing to re-process original video content during detection operations.
2Reliability
If manual policing methods are used to identify copyright violations, then detection reliability is improved, but productivity and scalability deteriorate
Solution Approach 1:
The system implements self-service through automated machine learning classifiers that independently analyze channel features and predict copyright violation likelihood without human intervention. The trained models automatically process new channels, providing scalable detection while maintaining consistent reliability through the learned patterns from historical data.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated computational classifiers. The machine learning models substitute human analysts, processing channels at machine speed while maintaining detection reliability through training on historical piracy patterns.
3Measurement precision
If watermarking techniques are used for copyright detection, then detection accuracy is improved, but device complexity and operational requirements increase
Solution Approach 1:
Instead of embedding watermarks in original content and searching for them (traditional approach), the patent inverts the approach by analyzing channel features and behavior patterns to predict piracy likelihood. This eliminates the need for watermarking infrastructure while achieving detection through alternative mathematical modeling.
4Measurement precision
If complete video content analysis is performed to detect piracy, then measurement precision is improved, but processing time and computational energy increase
Solution Approach 1:
The system extracts only relevant features from complete video content (metadata, channel statistics, engagement metrics) rather than analyzing entire video files. This extraction enables precise detection predictions without the computational burden of processing full video content.
Solution Approach 2:
The patent applies partial action by analyzing only the most discriminative channel features necessary for piracy detection rather than performing exhaustive analysis of all video properties. The trained classifier identifies and uses only the features that most strongly correlate with piracy behavior.
Data Source
AI summary
The herein disclosed technology provides methods and systems that utilize machine learning solutions to identify web-based channels that are sources pirated copyright material, such as by using a machine learning classifier that is trained on historical copyright piracy data and channel features that may be determined and analyzed for each of a collection of channels without analyzing video or audio content of the channel.