About This Architecture

Deepfake detection pipeline that ingests user-uploaded images or videos, preprocesses media input, extracts forensic features, and runs a trained detection model to classify content as authentic or synthetic. The system branches on model output to generate either a FAKE or REAL result report, then displays findings to the user. This architecture demonstrates best practices for real-time media verification, critical for content moderation, forensic investigation, and platform trust. Fork this flowchart on Diagrams.so to customize preprocessing steps, integrate your own detection model, or add confidence scoring and audit logging. Consider adding model versioning and A/B testing stages for production deepfake detection systems.

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How does a deepfake detection system work end-to-end?

A deepfake detection system accepts user-uploaded images or videos, preprocesses the media, extracts forensic features, and runs a trained detection model to classify content as FAKE or REAL. The system branches on the model's output to generate appropriate result reports and displays findings to the user, enabling real-time media verification for content moderation and forensic applications.