Adaptive Chunked Video Fingerprinting for Content Authentication
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Solution Overview
Problem
Existing video authentication systems struggle to efficiently authenticate longer duration videos and are vulnerable to manipulation, such as deep fakes, due to scalability challenges and inefficiencies in indexing and searching rich multi-modal features.
Innovation Solution
A scalable content authentication system that adaptively reduces the number of video chunks based on semantic similarity, uses product quantization for descriptor compression, and employs efficient indexing through similarity search libraries like FAISS to maintain accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every video chunk is indexed and searched in the trusted database, then video authentication accuracy is improved, but processing time and resource requirements increase significantly
Solution Approach 1:
The patent divides long videos into smaller chunks and processes them independently. By segmenting the video authentication task, the system can efficiently handle large videos without requiring to index and search every single chunk against the entire database, thus reducing processing time while maintaining authentication accuracy through selective chunk matching.
Solution Approach 2:
The patent applies partial action by not requiring all video chunks to be matched for authentication. Instead, it suffices to find matches for a sufficient subset of chunks to establish video authenticity. This partial matching approach reduces the search space and processing requirements while maintaining reliable authentication results.
2Measurement precision
If the trusted database index grows to include all video chunks, then matching accuracy is improved, but device complexity and scalability worsen
Solution Approach 1:
The patent extracts and indexes only the most distinctive and informative video chunks rather than all chunks. By selecting representative chunks that capture essential video content and features, the system maintains high matching accuracy while significantly reducing the size and complexity of the trusted database index, enabling better scalability.
Solution Approach 2:
The patent applies different processing qualities to different video chunks based on their importance and content characteristics. Rather than uniformly indexing all chunks with the same level of detail, the system applies selective indexing strategies that focus computational resources on chunks that provide the most value for authentication, reducing overall index complexity while preserving matching accuracy.
3Reliability
If rich multi-modal features are used for video authentication, then authentication reliability is improved, but processing complexity and resource requirements increase
Solution Approach 1:
The patent performs preliminary processing of video features before authentication, including extraction of audio-visual features, synchronization, and preliminary filtering. By preparing and organizing multi-modal features in advance, the system reduces the complexity of the actual authentication process while maintaining high reliability through comprehensive feature analysis.
Data Source
AI summary
Embodiments are disclosed for performing content authentication. A method of content authentication may include dividing a query video into a plurality of chunks. A feature vector may be generated, using a fingerprinting model, for each chunk from the plurality of chunks. Similar video chunks are identified from a trusted chunk database based on the feature vectors using a multi-chunk search policy. One or more original videos corresponding to the query video are then returned.