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(Features, Data Flow, Target Users, Timeline, Problem Statement,…
Features
AI Analysis Engine
Deepfake detection
Frame-level artefact analysis
Audio-video mismatch
Results Dashboard
Authenticity score
Suspicious frames
Confidence level
Video Upload
Upload from gallery
Record using camera
User Account System
Login/Signup
History
Notifications
Completion alerts
Data Flow
User uploads video
Backend processes frames
AI model analyses
Score returned
Target Users
Students
Journalists
Content creators
General public
Timeline
Week 1–2: Research
Week 3–5: UI/UX
Week 6–10: Integration
Week 11–12: Testing
Problem Statement
Rise of AI-generated fake videos
Difficulty in manual verification
Need for fast & reliable detection
Objectives
Detect AI-generated/manipulated videos
Provide authenticity score
Easy user interface
Technology Stack
Frontend (Flutter / React Native)
Backend (Python FastAPI / Flask)
AI Model (CNN/RNN/Transformer)
Success Metrics
Detection accuracy
User adoption
Processing speed