Face-Based Query Language for Synthetic Media Detection
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Solution Overview
Problem
Existing technologies fail to efficiently manage and identify synthetic media, such as deepfake videos, in large media repositories, and struggle to integrate media information with programmatic functions, making it difficult to reliably query and analyze images and videos.
Innovation Solution
A face-based query language (FQL) system that enables efficient querying and analysis of media by transforming natural language queries into structured FQL queries, selecting appropriate artificial intelligence models, and integrating with programmatic operations to automatically identify and analyze media properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large repositories of media are stored for training AI detection tools, then the ability to detect synthetic media improves, but the difficulty of tracking and managing information in the repository increases
Solution Approach 1:
The system performs preliminary actions by automatically generating metadata, tags, and summaries for media files during ingestion, rather than requiring manual organization later. This preliminary structuring of data makes future queries and management tasks significantly easier while maintaining reliable detection capabilities.
Solution Approach 2:
The patent introduces an intermediary layer of structured metadata and indexing between the raw media repository and the detection AI tools. This intermediary structure acts as an organized interface that simplifies access and management of the underlying complex media storage without compromising detection accuracy.
2Measurement precision
If manual review of stored videos is performed to identify individuals, then accurate information can be obtained, but the processing time and resources required increase significantly
Solution Approach 1:
The system replaces the mechanical process of manual video review with automated AI-based face recognition and analysis tools. These computational systems can rapidly scan and identify individuals in media files with high accuracy, eliminating the time-consuming manual review process while maintaining or improving identification precision.
3Loss of information
If comprehensive media analysis is performed on all stored videos, then complete information is obtained, but the computing resources required become excessive
Solution Approach 1:
The system applies partial action by performing analysis only on portions of media that are relevant to specific queries or detection needs, rather than comprehensively analyzing every byte of stored media. This selective approach uses AI models to identify and analyze only the necessary segments, maintaining information completeness for detected content while dramatically reducing overall computing resource consumption.
Data Source
AI summary
A user device transmit a natural language query with a request for a description of videos stored in a media repository. A query system receives the query and determines a command associated with obtaining the requested description of the videos stored in the media repository requested by the query. The determined command corresponds to an image analysis to perform on at least a portion of the stored videos. The query system determines, based at least in part on the determined command, an artificial intelligence model to execute on at least the portion of the stored videos. The query system determines, by executing the determined artificial intelligence model, a model output that includes the requested description of the videos stored in the media repository. The query system provides a response to the query. The response includes the requested description of the videos stored in the media repository.