About This Architecture

Deepfake detection pipeline ER diagram models a multi-stage analysis workflow that ingests video inputs, preprocesses facial regions, and applies specialized consistency analyzers to lip-voice sync, mouth movement, and jaw-chin dynamics. Data flows from User through VideoInput to Preprocessor, which extracts facial regions feeding three parallel consistency analyzers and a quality scorer, all converging in DecisionEngine to compute a final suspicion score. This architecture demonstrates best practices for modular detection logic, enabling practitioners to isolate and tune individual analysis components independently. Fork this diagram on Diagrams.so to customize analyzer chains, add new facial region detectors, or integrate alternative scoring methods for your deepfake detection deployment.

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How should I structure a deepfake detection system database and processing pipeline?

This ER diagram shows a modular deepfake detection architecture where VideoInput flows through Preprocessor to extract facial regions, then branches into three parallel consistency analyzers (lip-voice sync, mouth movement, jaw-chin dynamics) plus a quality scorer. All results feed into DecisionEngine, which computes a final suspicion score and generates an AnalysisReport.