ML Piracy Detection for Real-Time Streaming Media
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Solution Overview
Problem
Current techniques for detecting and remediating digital piracy are unreliable, cumbersome, and ineffective in real-time, particularly for live broadcasts and streaming media, due to the alteration or removal of fingerprints and watermarks, and the need for pre-existing databases.
Innovation Solution
A machine-learning based digital piracy detection system using a pattern recognizer with trained machine-learning models to identify multimedia features and patterns, enabling real-time detection and remediation without exact matches, and employing a confidence threshold for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If current matching techniques are used for piracy detection, then detection accuracy can be maintained through exact matches, but detection speed becomes time-consuming and real-time detection is not achievable
Solution Approach 1:
The patent replaces traditional mechanical matching techniques with machine learning-based pattern recognition. The system uses trained machine learning models to automatically identify pirated content through pattern recognition rather than exact matching, enabling real-time detection while maintaining accuracy. This substitution allows the system to process content at speeds comparable to live broadcasts and streaming media.
2Reliability
If pre-stored databases are used for piracy detection, then detection reliability can be improved through exact matches, but device complexity and storage requirements increase
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance on legitimate content. These pre-trained models are then deployed to detect piracy without requiring continuous access to large databases. The models learn patterns from training data and can independently identify pirated content, reducing the need for complex storage infrastructure while maintaining detection reliability.
3Loss of time
If current techniques are applied to live broadcasts and streaming media, then piracy detection can be performed, but the lag between detection and live media reduces the ability to detect and thwart pirated content in real-time
Solution Approach 1:
The patent implements dynamic detection by processing streaming and live broadcast content in real-time as it is transmitted. The machine learning models analyze content continuously during playback rather than requiring post-processing, enabling the system to detect and respond to piracy incidents as they occur. This dynamic approach eliminates detection lag and allows immediate throttling of pirated streams.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for detecting and preventing digital media piracy. In example aspects, a machine learning model is trained on a dataset related to digital media content. Input data may then be collected by a data collection engine and provided to a multimedia processor. The multimedia processor may extract multimedia features (e.g., audio, visual, etc.) and recognized patterns from the input data and provide the extracted multimedia features to a trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as pirated digital media. Remedial action response(s) may subsequently be deployed to thwart the piracy of the digital media.