Authentic Image ──> Generative AI Inpaint (Flux / Firefly) ──> Instant Refund Bot ──> Payout Leakage ($$$)
                                  │
                                  ▼
                         [ VERISIGHT MCP ]
               • Sub-3ms Physical Sensor Analysis
               • Mathematical Invariant Checks
               • Autonomous Auto-Block & S3 Vault

flowchart TD
    A["📸 Incoming Claim Image (REST / MCP / CLI)"] --> B["Active Perception: CIE LAB Luminance Check"]
    B --> C{"Lighting Optimal?"}
    C -- "No (< 65/255)" --> D["Adaptive CLAHE Contrast Normalization"]
    C -- "Yes" --> E["Multi-Spectral Forensic Suite"]
    D --> E

    subgraph E["Multi-Spectral Mathematical Forensic Pipeline"]
        E1["Gradient-Normalized ELA (Sobel Compensated)"]
        E2["PRNU CMOS Sensor Noise Extraction"]
        E3["Azimuthal 2D FFT Diffusion Lattice Harmonics"]
        E4["Multi-Q JPEG Quantization Ghost Sweeper"]
        E5["Perceptual Hash Sybil Duplicate Detection"]
        E6["2D Recapture Presentation Attack Detection (PAD)"]
    end

    E1 & E2 & E3 & E4 & E5 & E6 --> F["Autonomous Concordance Gate"]
    F --> G{"Concordance Verdict"}
    
    G -- "Risk < 35%" --> H["🟢 AUTO_APPROVE_CLAIM (Instant Payout)"]
    G -- "35% <= Risk < 70%" --> I["🟡 HUMAN_IN_THE_LOOP_REVIEW"]
    G -- "Risk >= 70%" --> J["🔴 REJECT_SYNTHETIC_TAMPER (Auto-Blocked)"]
    
    H & I & J --> K["Cryptographic Audit Dossier (SHA-256 + S3 / DynamoDB)"]

from backend import scan_image, verify_claim

# 1-Line multi-spectral forensic audit
report = scan_image("claims/produce_claim.jpg", claim_type="Fresh Produce")

print(f"Verdict: {report['verdict']} ({report['tamper_probability']}% Risk)")
print(f"Suspect ROI: {report['roi']} | ELA Score: {report['metrics']['ela_score']}")
print(f"Action: {report['recommended_action']}")

# Instant forensic audit of an image
verisight scan path/to/voucher.jpg

# Output machine-readable JSON telemetry
verisight scan path/to/invoice.jpg --json

# Run reproducible ARM64 COOL performance benchmark
verisight benchmark --frames 100

# Launch MCP Server and FastAPI Backend Daemon
verisight server --port 8000

{
  "mcpServers": {
    "verisight": {
      "command": "python",
      "args": ["-m", "backend.cli", "server", "--mcp"]
    }
  }
}

======================================================================
 VERISIGHT MCP — REPRODUCIBLE OPENCV 5 COOL BENCHMARK
======================================================================
Platform Architecture: aarch64 (AWS Graviton3 / c7g.medium)
OpenCV Build Version : 5.0.0 (Arm64 COOL KleidiCV Enabled)
Binary Source File   : /opt/cool/python_3.12/site-packages/cv2/__init__.py
Executing 100 continuous perception-decision pipeline frames...

--- REPRODUCIBLE BENCHMARK RESULTS TABLE ---
| Metric                        | AWS Graviton (Arm64 + COOL) | Baseline x86 Unoptimized | Delta / Speedup     |
| :---------------------------- | :-------------------------- | :----------------------- | :------------------ |
| **Mean Frame Latency**        | **2.62 ms**                 | 3.72 ms                  | **+42.0% Faster**   |
| **95th Percentile Latency**   | **3.10 ms**                 | 4.40 ms                  | **Guaranteed SLA**  |
| **Forensic Throughput**       | **381.6 FPS**               | 268.8 FPS                | **Enterprise Scale**|
| **KleidiCV NEON Acceleration**| **ACTIVE (/opt/cool)**      | Disabled                 | **Hardware Arm64**  |
======================================================================

verisight/
├── .github/
│   ├── workflows/ci.yml         # Automated Python 3.11 & Next.js CI
│   ├── ISSUE_TEMPLATE/          # Bug reports & feature requests
│   └── PULL_REQUEST_TEMPLATE.md # Contributor review standards
├── backend/
│   ├── __init__.py              # Python SDK exports (scan_image, verify_claim)
│   ├── cli.py                   # Terminal CLI entry point (verisight command)
│   ├── agent.py                 # Autonomous Perception-Hypothesis-Action loop
│   ├── forensics.py             # Pure OpenCV 5 multi-spectral inspection primitives
│   ├── mcp_server.py            # Model Context Protocol JSON-RPC implementation
│   ├── main.py                  # FastAPI REST endpoints & WebSocket feeds
│   ├── cloud_storage.py         # AWS S3 & DynamoDB evidence persistence
│   └── test_sanity.py           # End-to-end automated sanity verification suite
├── frontend/                    # Next.js 16.4.3 enterprise dark-mode dashboard
│   ├── app/                     # React Server Components & UI layouts
│   └── components/              # Interactive 4x ROI Loupe, Webcam, & Spectrum viewers
├── docker-compose.yml           # Production multi-stage container orchestration
├── Dockerfile.arm64             # AWS Graviton COOL deployment container
├── deploy_graviton.sh           # Automated EC2 setup script
├── pyproject.toml               # Standalone package build (pip install verisight)
├── mcp_claude_desktop.json      # Ready-to-use Claude Desktop configuration
├── CITATION.cff                 # Academic & industry citation metadata
├── CONTRIBUTING.md              # Contributor guidelines & code style
├── SECURITY.md                  # Vulnerability disclosure policy
├── LICENSE                      # Apache 2.0 Open-Source License
└── README.md

git clone https://github.com/Adityarajtiwari880/verisight.git
cd verisight
docker compose up --build

# 1. Clone repository
git clone https://github.com/Adityarajtiwari880/verisight.git
cd verisight

# 2. Start the FastAPI OpenCV 5 Backend (Terminal 1)
cd backend
python -m pip install -r requirements.txt
python -m uvicorn main:app --host 127.0.0.1 --port 8000 --reload

# 3. Start the Next.js Frontend Dashboard (Terminal 2)
cd ../frontend
npm install
npm run dev

# 1. SSH into your AWS Graviton instance
ssh -i "your-key.pem" ubuntu@<YOUR-EC2-PUBLIC-IP>

# 2. Clone repo and run deployment script
git clone https://github.com/Adityarajtiwari880/verisight.git
cd verisight
chmod +x deploy_graviton.sh
./deploy_graviton.sh

# 3. Verify hardware acceleration status
curl http://127.0.0.1:8000/api/aws-status

@software{tiwari2026verisight,
  author       = {Tiwari, Aditya Raj},
  title        = {VeriSight MCP: Autonomous Physical AI Visual Forensics Engine},
  year         = {2026},
  publisher    = {GitHub},
  url          = {https://github.com/Adityarajtiwari880/verisight}
}