Multimedia Piracy Detection Using ML Pattern Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital piracy detection techniques are unreliable, cumbersome, and ineffective in real-time detection, especially for live broadcasts and streaming media, due to the need for exact matches and vulnerability to alterations in digital content.
Innovation Solution
A machine-learning based digital piracy detection system using a pattern recognizer with trained models to identify multimedia features and patterns, allowing for real-time detection and remediation without exact matches, utilizing machine learning algorithms like linear regression, logistic regression, and neural networks to generate confidence values for piracy detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current matching techniques are used for piracy detection, then exact matches can be identified, but the detection becomes time-consuming and unreliable for live broadcasts and streaming media
Solution Approach 1:
The patent transforms the piracy detection approach from exact matching to pattern recognition by changing the parameters used for comparison. Instead of requiring identical content matches, the system extracts multimedia features (visual, audio, metadata) and compares patterns across different representations of the same content, enabling both accuracy and speed for live and recorded media
Solution Approach 2:
The patent replaces traditional mechanical matching algorithms with machine learning-based pattern recognition systems. The machine learning model is trained to identify pirated content patterns without requiring exact matches, significantly reducing detection time while maintaining or improving accuracy for both recorded and live/streaming content
2Reliability
If traditional matching techniques are implemented, then piracy can be detected in recorded content, but the system is cumbersome and ineffective for live broadcasts and streaming media
Solution Approach 1:
The patent segments the piracy detection system into distinct modules: feature extraction components that identify multimedia characteristics, a machine learning model that processes these features, and a decision-making component that determines piracy status. This segmentation reduces overall system complexity while improving reliability for live and recorded content detection
Solution Approach 2:
The patent introduces multimedia feature extraction as an intermediary layer between the original content and the piracy detection process. This intermediary transforms complex multimedia content into comparable feature representations, simplifying the detection process and improving reliability across different content types including live broadcasts and streaming media
3Measurement precision
If exact match techniques are used, then piracy detection can be performed, but the system is vulnerable to alterations in digital content
Solution Approach 1:
The patent changes the matching parameters from exact content identity to pattern similarity based on extracted multimedia features. The machine learning model learns to recognize patterns that persist through various alterations (compression, formatting, minor edits) while maintaining precision in identifying pirated content
Solution Approach 2:
The patent creates a detection system that is 'porous' to content variations - it allows for differences in format, compression, and minor alterations while still detecting the underlying pirated content pattern. The feature extraction and pattern recognition approach filters out irrelevant variations and focuses on identifying the core copyrighted material
Data Source
Figure 1
Figure 2
Figure 3
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.