A comprehensive AI-powered platform for detecting and analyzing synthetic content across multiple media types with a cyberpunk-themed interface and real-time intelligence capabilities.
- Text Analysis: Linguistic pattern detection, GPT fingerprinting, semantic verification
- Image Forensics: Deepfake detection, pixel-level analysis, GAN artifact identification
- Audio Verification: Voice cloning detection, spectral analysis, waveform authentication
- Video Analysis: Temporal consistency checking, face-swap detection, motion vector analysis
- Cyberpunk Theme: Futuristic design with neon effects and animations
- Responsive Design: Works across desktop and mobile devices
- Real-time Updates: Live data streaming and dynamic content
- Interactive Dashboards: Comprehensive analytics and reporting
- Threat Intelligence: Global threat monitoring and geospatial analysis
- Real-time Analytics: Live detection statistics and performance metrics
- Source Verification: Content authentication and verification
- Comprehensive Reporting: Detailed analysis reports and export options
- Python 3.7 or higher
- pip package manager
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Clone or download the project
git clone <https://github.com/Uchiha-byte/TruthScan.git> cd TruthScan
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Install dependencies
pip install -r requirements.txt
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Start the application
python start.py
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Access the platform
- Main Interface: http://localhost:5000
- Dashboard: http://localhost:5000/dashboard
- Detection: http://localhost:5000/detection
- Intelligence: http://localhost:5000/intelligence
- Reports: http://localhost:5000/reports
TruthScan/
├── backend.py # Flask backend server
├── index.html # Main landing page
├── start.py # Application startup script
├── requirements.txt # Python dependencies
├── README.md # This file
├── CODEBASE_INDEX.md # Detailed codebase documentation
└── templates/ # Flask templates
├── base.html # Base template with shared styling
├── dashboard.html # Dashboard page
├── detection.html # Content detection interface
├── intelligence.html # Threat intelligence dashboard
└── reports.html # Analytics and reports
- Flask: Web application framework
- Flask-CORS: Cross-origin resource sharing
- Pillow: Image processing library
- Python 3.7+: Programming language
- HTML5: Semantic structure
- CSS3: Advanced styling with animations
- Tailwind CSS: Utility-first CSS framework
- JavaScript: Interactive functionality
- Google Fonts: Typography (Orbitron, Exo 2)
- Color Palette: Cyberpunk theme (cyan, purple, pink)
- Typography: Orbitron (headings), Exo 2 (body)
- Animations: CSS keyframes and transitions
- Effects: Neon glows, holographic cards, particle systems
POST /api/analyze- Analyze uploaded contentGET /api/threats- Get threat intelligence dataGET /api/dashboard- Get dashboard statisticsGET /api/intelligence- Get intelligence dataGET /api/reports- Get reports and analytics
/- Main landing page/dashboard- Command dashboard/detection- Neural detection chamber/intelligence- Global threat matrix/reports- Analytics and reports
- Hero section with animated title
- Content type selection cards
- Analysis panel with real-time results
- Global threat intelligence overview
- System status monitoring
- Recent detection activity
- Threat distribution charts
- Performance metrics
- Multi-modal content analysis
- Real-time forensic results
- Model status monitoring
- Interactive analysis tools
- Geospatial threat mapping
- Real-time threat feed
- Source analysis and verification
- Temporal activity tracking
- Daily/weekly trend analysis
- Performance metrics
- Detection accuracy by type
- Export options (PDF, CSV, API)
- Perplexity analysis
- Burstiness detection
- Semantic coherence checking
- GPT fingerprinting
- GAN artifact detection
- Noise pattern analysis
- Compression artifact identification
- Color space analysis
- Voice cloning detection
- Spectral anomaly analysis
- Temporal inconsistency detection
- Acoustic fingerprinting
- Frame-level analysis
- Motion vector analysis
- Face swap detection
- Temporal artifact identification
The platform includes realistic prototype data including:
- 2,847 active threats globally
- 156 deepfakes detected
- 98.7% accuracy rate
- 45,000 verified sources
- Real-time threat intelligence feeds
- Geospatial threat clustering
- Performance metrics and analytics
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Run the startup script
python start.py
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The script will automatically:
- Install required dependencies
- Start the Flask backend server
- Open your browser to the platform
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Navigate through the interface:
- Use the navigation bar to access different sections
- Try the detection features with sample content
- Explore the intelligence dashboards
- View real-time analytics and reports
python backend.py- Backend: Add new routes in
backend.py - Frontend: Create new templates in
templates/ - Styling: Update CSS in
templates/base.html - API: Add new endpoints for data
- Colors: Modify CSS variables in
base.html - Animations: Update keyframe definitions
- Layout: Adjust Tailwind CSS classes
- Effects: Customize particle and glow systems
- Processing Time: 0.5-2.0 seconds per scan
- System Uptime: 99.9%
- API Response: <100ms average
- Concurrent Users: Supports multiple simultaneous analyses
- Content fingerprinting with SHA256 hashing
- Content verification and authentication
- Source validation and tracking
- Threat intelligence integration
- Real-time monitoring and alerting
This project is a prototype demonstration of Defending Digital Truth Through Intelligent Content Verification capabilities. All code and designs are for educational and demonstration purposes.
TruthScan - Advanced AI-powered Defending Digital Truth Through Intelligent Content Verification and verification platform.