CNN Video Fingerprinting for Modified Content Detection
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Solution Overview
Problem
Detecting and handling user-generated content that contains copyrighted material, especially when modified to evade detection, poses a significant technical challenge in online content management systems.
Innovation Solution
A method utilizing a convolutional neural network (CNN) to generate fingerprints for reference and provided content, comparing these fingerprints to determine if the provided content contains the asset, and enforcing associated policies if a match is found.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional content detection methods are used, then the system can identify unmodified copyrighted content, but it fails to detect modified content that has been cropped, reversed, or otherwise altered to evade detection
Solution Approach 1:
The patent transforms video content into a different parameter space using CNNs, converting visual data into fingerprint representations that are invariant to common modifications like cropping, reversing, and filtering. This parameter transformation allows the system to maintain high detection accuracy across modified content variants.
Solution Approach 2:
The system creates fingerprint copies of the video content that capture essential features while being resistant to modification. These fingerprints serve as robust representations that can be compared to identify matches even when the original visual content has been altered.
2Measurement precision
If the system processes all user-generated content through detailed analysis, then it can accurately identify copyrighted material, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent extracts essential features from video content to create compact fingerprint representations. Instead of analyzing entire videos, the system extracts key frames and generates condensed fingerprint data that retains identification capability while dramatically reducing computational requirements.
Solution Approach 2:
The system replaces complex mechanical content analysis with neural network-based fingerprint generation and comparison. The CNN-based approach substitutes traditional frame-by-frame visual analysis with a more efficient mathematical transformation process that is faster and less computationally intensive.
3Reliability
If the system uses modified content in training data, then it can learn to recognize altered copyrighted material, but it may also learn to detect legitimate transformed content incorrectly
Solution Approach 1:
The patent applies different processing and attention mechanisms to different parts of the training data, emphasizing locally distinctive features that are characteristic of copyrighted content while being invariant to common modifications. This selective feature emphasis helps distinguish copyrighted material from legitimately transformed content.
Data Source
AI summary
Provided content is determined to contain an asset represented by reference content by comparing digital fingerprints of the provided content and the reference content. The fingerprints of the reference content and the provided content are generated using a convolutional neural network (CNN). The CNN is trained using a plurality of frame triplets including an anchor frame representing the reference content, a positive frame which is a transformation of the anchor frame, and a negative frame representing content that is not the reference content. The provided content is determined to contain the asset represented by the reference content based on a similarity measure between the generated fingerprints. If the provided content is determined to contain the asset represented by the reference content, a policy associated with the asset is enforced on the provided content.