Audio Fingerprint Segmentation for Ownership Verification
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Solution Overview
Problem
Conventional techniques for proving ownership of media files are flawed as they can be easily transferred and vary among different copies, even with minor changes in bitrate or metadata, making it difficult to authenticate genuine ownership.
Innovation Solution
A system that uses a random number to segment and permute audio files, generating an audio fingerprint that remains consistent across different bitrates and variations, allowing users to prove ownership by matching the fingerprint with a server's reference fingerprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If information generated from audio files is used as proof of ownership, then ownership verification is enabled, but the proof is easily transferable to other users without the audio files
Solution Approach 1:
The audio file is divided into multiple segments, and a segment tree data structure is constructed where each node represents a segment or combination of segments. This segmentation ensures that to verify ownership, the system must process the entire hierarchical structure, making it computationally infeasible to transfer or replicate the proof without the original file.
Solution Approach 2:
A segment tree is built where segments are nested hierarchically - each parent node contains references to child nodes representing sub-segments. This nested structure creates multiple layers of verification, where each level depends on the previous level, ensuring that ownership proof cannot be separated or transferred independently.
2Adaptability or versatility
If media files are encoded at different bitrates, then variety in media consumption is enabled, but different information is generated for different copies of the same media
Solution Approach 1:
The segment tree construction process is designed to be invariant to bitrate changes. By focusing on temporal segmentation rather than bitrate-dependent features, the system generates consistent segment identifiers across different encoding parameters, allowing the same ownership proof to validate multiple versions of the same media file.
Solution Approach 2:
The segment tree structure serves multiple functions: it verifies ownership, handles different bitrates, and works across various media formats. The hierarchical segmentation approach creates a universal proof mechanism that adapts to different encoding parameters while maintaining verification consistency.
3Loss of information
If metadata is used for ownership proof, then additional information is available for verification, but different metadata causes different information to be generated for different copies
Solution Approach 1:
The ownership verification system extracts only the essential temporal and spectral features from the audio signal, excluding metadata entirely. By focusing on the core audio content's structural properties rather than associated metadata, the system generates consistent verification information that is independent of variable metadata fields.
Data Source
AI summary
A robust digital fingerprint of a file ensures that one able to produce the robust digital fingerprint has possession of the file. A client obtains information that is unpredictable to the client and uses that information to modify the file and generate a robust digital fingerprint from the modified file. A server, with access to the same unpredictable information, verifies the generated robust digital fingerprint. An algorithm for generating the robust digital fingerprint has a property that different representations of the same content will produce matching digital fingerprints.