CDN Video Stream Monitoring for Real-Time Piracy Detection
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Solution Overview
Problem
Conventional digital rights management (DRM) tools are not fool-proof and fail to effectively prevent unauthorized distribution and rebroadcasting of streaming media, leading to economic loss and security risks.
Innovation Solution
A system utilizing a machine-learning model to monitor streaming activity, detect piracy patterns in real-time, and perform mitigating actions such as blocking the stream or terminating access, by analyzing user and account data through a content delivery network (CDN) and a trained machine-learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional DRM tools are used to protect streaming media, then some level of protection is provided, but they fail to effectively prevent unauthorized distribution and rebroadcasting
Solution Approach 1:
The patent introduces a CDN-based monitoring system as an intermediary between the content delivery infrastructure and piracy prevention. This intermediary layer captures activity data from CDN nodes, enabling effective piracy detection without requiring direct modification of existing DRM tools or content delivery mechanisms.
Solution Approach 2:
The patent replaces conventional mechanical DRM protection mechanisms with a data-driven, machine-learning-based monitoring system. Instead of relying on traditional access control and encryption methods, the system uses activity pattern analysis and behavioral biometrics to detect piracy, achieving better effectiveness while reducing false positives.
2Measurement precision
If real-time monitoring of streaming activity is implemented, then piracy detection capability is improved, but system resource consumption and complexity increase
Solution Approach 1:
The patent merges piracy monitoring functionality with the existing CDN infrastructure. By utilizing the CDN's distributed network of nodes and their existing data collection capabilities, the system achieves real-time monitoring without building a separate complex infrastructure. The monitoring is integrated into the content delivery flow, sharing resources and reducing overall system complexity.
Solution Approach 2:
The machine-learning model operates autonomously, continuously learning from activity data and automatically updating its understanding of piracy patterns. The system self-trains on new data, adapts to emerging piracy techniques, and maintains detection accuracy without requiring manual intervention or complex reconfiguration, reducing operational complexity.
3Measurement precision
If machine-learning models are used to analyze activity data, then false positives are reduced, but computational requirements and processing time increase
Solution Approach 1:
The machine-learning model is pre-trained on extensive activity data representing both legitimate and pirated streaming patterns. This preliminary training establishes a robust baseline for detection, enabling the model to quickly evaluate new activity data in real-time without requiring extensive computational resources during actual piracy detection events.
Solution Approach 2:
The patent segments the monitoring system into distributed components across the CDN network. Activity data is collected and processed at multiple CDN nodes in parallel, with the machine-learning model receiving segmented streams of data rather than processing all data centrally. This segmentation reduces processing time and computational load at any single point while maintaining detection accuracy.
Data Source
AI summary
Systems and methods for performing video piracy monitoring are disclosed. One computer-implemented method may include: detecting, at a digital media platform associated with a piracy monitoring system, a request to transmit a video stream to a user computing device associated with a user; transmitting the video stream to the user computing device and responsive to determining that the user is a subscriber to the digital media platform; retrieving activity data associated with the video stream using a content delivery network (CDN) associated with the piracy monitoring system; providing, responsive to the retrieving, the activity data to a trained machine-learning model configured to recognize a video piracy pattern; and performing a mitigating action to address piracy of the video stream responsive to receiving an output result from the trained machine-learning model indicating that the activity log data shares a predetermined level of similarity with the video piracy pattern.