Media Modification Reversal for Efficient Copyright Identification
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Solution Overview
Problem
Existing media content identification systems face inefficiencies and high computational costs due to the need to analyze and identify altered media content items, which require numerous resource-intensive transactions to determine the presence of known works, especially in user-generated content platforms.
Innovation Solution
Implementing a machine learning model to identify and reverse modifications in media content items before comparing them to known works, reducing the number of resource-intensive operations needed to detect and license media content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional media content identification systems analyze altered media content items directly, then they can identify known works, but the computational cost and processing time increase substantially
Solution Approach 1:
The system performs preliminary actions by detecting modifications in the media content item before conducting the identification process. A machine learning model analyzes the media content to identify alterations such as speed changes, pitch modifications, or other transformations. Once detected, the system reverses these modifications to restore the media content to its original state, thereby preparing it for efficient identification against the known works database without requiring computationally expensive direct matching of altered content.
2Reliability
If the system evaluates every uploaded media content item to identify known works, then copyright compliance is ensured, but processing time and resource usage increase
Solution Approach 1:
Before evaluating media content items for copyright compliance, the system performs preliminary modification detection using a machine learning model. This preliminary step identifies whether the content has been altered and automatically reverses detected modifications. By preparing the content in advance, the subsequent identification process becomes faster and more efficient, maintaining reliable copyright compliance checks while improving overall processing speed and reducing resource consumption.
3Adaptability or versatility
If users alter portions of known works in uploaded content, then aesthetic purposes are achieved or copyright evasion is attempted, but identification of the original work becomes more difficult
Solution Approach 1:
The system converts the harmful effect of content modification (which obscures the original work and complicates identification) into a beneficial process. The machine learning model is trained to recognize common modification patterns such as speed changes, pitch shifts, and other transformations. By detecting and reversing these modifications, the system actually improves identification accuracy compared to direct matching, as it restores the media content to its original state where matching algorithms can operate most effectively.
Data Source
AI summary
A system processed a representation of a media content item using a trained machine learning model to identify whether the media content item is a modified media content item. The trained machine learning model outputs an indication of one or more modifications of the media content item. Responsive to determining that the one or more modifications satisfy a criterion, one or more operations are performed on the media content item in view of the one or more modifications to generate an updated media content item. The updated media content item is compared to known media content items to identify known material contained therein.