InvisID is an elite forensic platform designed for high-confidence leak attribution. By combining True ASIFT alignment with a JND-Guided DWT-QIM architecture, InvisID ensures that sensitive digital assets can be traced back to their source even after extreme geometric distortion, lossy compression, or 8K resolution interpolation.
InvisID implements a multi-layered security posture to ensure both forensic integrity and system protection:
- True ASIFT Alignment: A multi-scale alignment engine that simulates multiple camera optical axes to perfectly resynchronize 8K assets after 3D perspective skew.
- JND-Guided DWT-QIM: Texture-aware frequency domain embedding that scales strength based on local complexity—ensuring absolute invisibility in smooth regions and extreme robustness in textured areas.
- Encryption at Rest (AES-256-GCM): All master assets are stored using authenticated encryption to prevent unauthorized disk access or tampering.
- HMAC Request Signing: Every UI transaction is cryptographically signed using HMAC-SHA256 to eliminate Man-in-the-Middle (MITM) and Replay attacks.
- Reed-Solomon RS Armor: Forensic identities are protected by high-capacity Error Correction Codes to recover data from noisy or corrupted extractions.
- Session Heartbeat: Real-time server instance tracking via unique IDs to enforce immediate security logout upon system restart.
- High-Fidelity PDF Reports: Professional forensic case export including ASIFT alignment proofs and visual evidence.
- Stress-Test Lab 4.0: Advanced adversarial simulation suite including WhatsApp Simulation, 3D Perspective Tilt, and Median Filtering.
- 8K Resolution Support: Fully vectorized NumPy-based engine capable of processing 33-megapixel images with zero performance degradation.
# Install dependencies
uv sync
# Setup environment
cp .env.template .env # Configure your MASTER_SECRET
# Start Development Server
uv run app/main.py
# Run Technical Forensic Audit (Preliminary Benchmark)
PYTHONPATH=. uv run scripts/forensic_ultimate_test.py# Build the image
docker build -t invisid .
# Run the container
docker run -d -p 8000:8000 --env-file .env --env HOST=0.0.0.0 invisid
# Health check
curl http://localhost:8000/healthAcademic Disclaimer: This project is a Cybersecurity Capstone currently in the Active Technical Validation phase. Current benchmarks are subject to further stress-testing.