MedContext: Contextual Authenticity Detector

Medical images don't need to be fake to cause harm.

📖 Start Here | 📊 Validation | 🏆 Submission | 🎬 Demo Video

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🎯 The Problem

Most people think medical misinformation looks like this:

From our comprehensive literature review we discovered:

The real problem: Authentic medical or health-related images repeatedly reused with false or misleading captions.

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🔬 Both Contextual Signals Are Necessary

MedContext was empirically motivated in its development, rather than feature-driven. We ran justification and validation studies to test whether single contextual signals could detect visual medical misinformation—they cannot.

Validation on Med-MMHL Benchmark (n=163):

We validated MedContext against the Med-MMHL (Medical Multimodal Misinformation Benchmark), a research-grade dataset of real-world medical misinformation in image-claim pairs curated from fact-checking organizations, with human-annotated, expert fact-checked labels. The validation used stratified random sampling (seed=42) of 163 samples from the Med-MMHL test set (1,785 total samples) to ensure representative label distribution (83% misinformation, 17% legitimate).

Note on Dataset Provenance: Med-MMHL is a separate benchmark from the datasets described below used to justify the studies. Med-MMHL contains real-world social media posts with fact-checked labels, whereas justification datasets (BTD+UCI) contain medical images with synthetically assigned contextual labels for controlled experimentation, not validation.

Results:

Individual Signals Insufficient: Veracity 73.6% · Alignment 90.8%

Hierarchical Optimization Unlocks The S-Curve: 91.4% Accuracy

Precision 96.9% · Recall 92.6% · F1 94.7% · Thresholds 0.65/0.30

What this proves:

• ✅ Veracity alone (73.6%) is insufficient — claim truth assessment misses image misuse (e.g., caterpillar labeled as HIV virus)
• ✅ Alignment alone (90.8%) is strong — image-claim consistency is the dominant signal; veracity provides a critical safety net
• ✅ Simple combination (90.8%) plateaus — naive thresholds provide minimal improvement over alignment alone
• ✅ Hierarchical optimization (91.4%) achieves the breakthrough — smart thresholds (0.65/0.30) with VERACITY_FIRST logic catch 3 critical edge cases alignment misses
• ✅ High precision (96.9%) and recall (92.6%) — catches the vast majority of misinformation cases with very few false alarms

Methodology: Results use the MedGemma 4B IT model (HuggingFace Inference API) with optimized decision thresholds (v<0.65 OR a<0.30) on the Med-MMHL validation set (n=163). Bootstrap confidence intervals: [87.7%, 94.5%]. Note: Thresholds were tuned on the same validation set used for final evaluation; reported 91.4% accuracy may be optimistic and not fully generalize to new data. Future work should validate on a held-out test set.

Key Insight: No signal is good enough on its own. Optimization provides a modest boost (0.6 pp), but when scaled to the impact of the actual threat (billions of users), the veracity fallback catches millions of messages of misinformation—only possible with MedContext's multimodal medical training (MedGemma).

📊 Full Validation Report | 📊 Validation Story (Interactive)

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✨ The Solution: Agentic AI for Contextual Authenticity

MedContext uses a 3-phase agentic workflow to assess whether image content aligns with its claim:

How It Works

Architecture Principle: "The doctor does doctor work, the manager does management work."

1. TRIAGE (Two-Step Process)
   • Medical Analysis: MedGemma assesses image + evaluates claim veracity and alignment
   • Tool Selection: LLM orchestrator decides which optional investigative tools to deploy
2. DYNAMIC DISPATCH - Selectively activates only necessary add-on tools
   • Reverse search (finds prior uses)
   • Forensics (pixel-level manipulation detection - optional add-on)
   • Provenance (blockchain-style verification)
3. SYNTHESIS - Orchestrator aggregates all evidence → contextual authenticity verdict with rationale

Not just "AI-powered"—truly autonomous decision-making with separated concerns.

See AGENTIC_WORKFLOW.md for complete pipeline visualization.

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🌍 Real-World Impact

Deployment Partner: HERO Lab, UBC

• Scientific Director: Jamie Forrest
• Scale: Potentially millions of users via Telegram bot integration

📈 See Impact Plan

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🚀 Quick Start for Judges

Setup (5 minutes)

# 1. Install dependencies (2 min)
uv venv && uv run pip install -r requirements.txt
cd ui && npm install && cd ..

# 2. Configure (1 min)
cp .env.example .env
# Add: MEDGEMMA_HF_TOKEN=hf_your_token

# 3. Run migrations (30 sec)
alembic upgrade head

# 4. Start backend (30 sec)
uv run uvicorn app.main:app --reload --app-dir src

# 5. Start frontend (30 sec, new terminal)
cd ui && npm run dev

Verify (1 minute)

# Run test suite
uv run pytest tests/ -v
# Expected: 65/65 passed ✅

# Test API
curl http://localhost:8000/health
# Expected: {"status": "ok"}

# Visit UI
# Open http://localhost:5173

Total Time: ~5 minutes from clone to running system

🐳 Docker Setup (Recommended for Judges - Easiest!)

One command to run everything:

# 1. Configure environment
cp .env.example .env
# Add: MEDGEMMA_HF_TOKEN=hf_your_token

# 2. Launch all services (database + backend + frontend)
docker-compose up -d

# Access at:
# - Frontend: http://localhost
# - Backend API: http://localhost:8000
# - API Docs: http://localhost:8000/docs

Why Docker? No dependency conflicts, works on any OS, production-ready setup.

See DOCKER_QUICKSTART.md for complete guide with troubleshooting.

🔐 Demo Access (For Public Deployments)

For Judges/Users accessing the public demo:

The live demo requires an access code to prevent abuse and control API costs.

Access Code: MEDCONTEXT-DEMO-2026

How to use:

1. Via UI Settings:

   • Click "Settings" in the top-right corner
   • Enter the access code in the "Demo Access Code" field
   • The code is stored locally in your browser

2. Via API (for developers):

   # Include header in requests
   curl -X POST http://your-demo-url/api/v1/orchestrator/run \
     -H "X-Demo-Access-Code: MEDCONTEXT-DEMO-2026" \
     -F "file=@image.jpg" \
     -F "context=Your context here"

Rate Limits:

• 10 requests per IP address per hour
• If you hit the limit, wait an hour or contact the developer

For Local Development:

• Leave DEMO_ACCESS_CODE empty in your .env file
• No access code required when running locally

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🏆 Competition Highlights

Primary Category: Agentic AI System

✅ Dynamic tool selection based on image triage ✅ Context-aware reasoning handles contradictory evidence ✅ Explainable verdicts with traceable rationale ✅ LangGraph integration for workflow visualization

Key Strengths

• ✅ Real-world optimization — Addresses authentic images with misleading context (the dominant threat pattern)
• ✅ Contextual authenticity focus — Detects misuse of genuine images, not just fake pixels
• ✅ Empirically proven approach — Demonstrated that hierarchical optimization (91.4%) achieves breakthrough performance over individual signals (73.6%/90.8%)
• ✅ Production deployment partner — Counterforce AI & the University of British Columbia for deployment with a web app and Telegram bot
• ✅ Production-ready implementation — 62/62 tests passing (3 skipped), comprehensive validation

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📊 Technical Highlights

Production-Ready Quality

• Code: 4,100+ lines Python, 527 lines React
• Tests: 62/62 unit tests passing (comprehensive test suite with mocked integrations)
• Architecture: FastAPI + React + PostgreSQL
• Security: Tool whitelist, prompt injection protection, SSRF prevention
• Providers: 5 MedGemma options (HuggingFace, LM Studio, llama-cpp, vLLM, Vertex AI)

Proof of Justification (Empirical Motivation)

PoJ	Dataset	Label Source	Method	Result
PoJ 3	BTD+UCI (160 image-claim pairs: 120 BTD + 40 UCI)	Synthetic labels	MedGemma contextual	Veracity 61.3% · Alignment 56.9%
PoJ 2	BTD+UCI (160 samples: 120 BTD + 40 UCI)	Synthetic labels	DICOM-native pixel forensics	97.5% image integrity (optional add-on)
PoJ 1	UCI only (326 DICOM images)	Original dataset	ELA (Layer 1)	49.9% — chance (wrong tool for format)

Dataset Descriptions:

• BTD+UCI (PoJ 2-3): Combined validation set (120 authentic MRI from BTD dataset + 40 tampered scans from UCI dataset) with synthetically assigned contextual labels (veracity, alignment) programmatically generated for controlled experimentation—not human fact-checked.
• UCI (PoJ 1): Standalone tampered medical imaging dataset used to test pixel forensics baseline performance.
• Med-MMHL (Primary Validation, n=163): Separate research-grade benchmark with human-annotated, expert fact-checked labels from real-world fact-checking organizations—used for final system validation (91.4% accuracy with MedGemma 4B IT).

Analysis:

• Method: Bootstrap resampling (1,000 iterations) for confidence intervals across all PoJ experiments
• PoJ 3 Finding (BTD+UCI synthetic labels): Demonstrated that veracity and alignment are distinct contextual dimensions, each insufficient alone (61.3% and 56.9% respectively)
• Med-MMHL Validation (fact-checked labels): Proved hierarchical optimization is key—91.4% optimized accuracy (MedGemma 4B IT) vs 73.6% veracity-only and 90.8% alignment-only on real-world misinformation. Neither signal alone is sufficient.
• Optional add-ons: PoJ 1-2 validated pixel forensics on manipulated-image datasets; not tested on Med-MMHL since all Med-MMHL images are authentic
• Key Distinction: PoJ experiments used synthetic labels for controlled hypothesis testing; Med-MMHL used fact-checked labels for real-world validation

Real Validation: Med-MMHL benchmark (human fact-checked) — see VALIDATION.md for full results

Novel Contributions

1. First empirical validation proving single contextual signals are insufficient, but hierarchical optimization achieves breakthrough performance:
   • On Med-MMHL (fact-checked labels): Veracity-only 73.6%, Alignment-only 90.8%, Optimized 91.4% (MedGemma 4B IT with VERACITY_FIRST + smart thresholds 0.65/0.30)
   • On BTD+UCI (synthetic labels): Demonstrated veracity (61.3%) and alignment (56.9%) are distinct dimensions requiring joint analysis
2. First agentic system for contextual authenticity assessment using MedGemma for combined veracity + alignment analysis
3. First deployment partnership for field validation (HERO Lab)

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📚 Documentation

For Judges - Recommended Reading Order:

1. START_HERE.md - Navigation guide (2 min)
2. EXECUTIVE_SUMMARY.md - One-page overview (2 min)
3. PROOF_OF_JUSTIFICATION.md - Empirical motivation (5 min) | VALIDATION.md - Validation hub (10 min)
4. SUBMISSION.md - Comprehensive submission (15 min)
5. AGENTIC_WORKFLOW.md - Technical deep dive (optional)

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🎬 Demo Video

Watch the 3-minute demonstration:

🎥 Watch on YouTube

Covers (3 minutes):

1. The Problem (authentic images used in fake or misleading contexts)
2. Med-MMHL Validation (n=163: individual signals 73.6%/90.8% vs optimized 91.4%)
3. Live Demo (upload → analysis → verdict)
4. Impact (Counterforce AI & UBC partnership and Telegram bot deployment)

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🛠️ Technology Stack

Backend:

• FastAPI (Python 3.12+)
• MedGemma (Google's medical LLM)
• LangGraph (agentic workflows)
• PostgreSQL + Alembic
• Redis (caching)

Frontend:

• React 19
• Vite
• Modern CSS

AI/ML:

• Multi-provider MedGemma (HuggingFace, LM Studio, llama-cpp, vLLM, Vertex AI)
• Gemini 2.5 Pro/Flash (LLM orchestration)
• PIL + NumPy (forensics)
• SerpAPI + Google Cloud Vision API (reverse image search)
• SHA-256 hash-chained provenance with optional Polygon blockchain anchoring

Infrastructure:

• Docker-ready
• PRAW (Reddit monitoring)
• Production-tested

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🔧 MedGemma Configuration

MedContext automatically routes requests based on the MEDGEMMA_MODEL name:

For Competition Judges (Recommended):

MEDGEMMA_MODEL=google/medgemma-1.1-4b-it
MEDGEMMA_HF_TOKEN=hf_your_token

Why: Minimal setup, no GCP account, easy to run

For Production Deployment:

MEDGEMMA_MODEL=vertex/medgemma
MEDGEMMA_VERTEX_PROJECT=your-project
MEDGEMMA_VERTEX_LOCATION=us-central1
MEDGEMMA_VERTEX_ENDPOINT=your-endpoint

Why: Lower latency, higher scale

Other Options:

• Quantized models (e.g., google/medgemma-1.1-4b-it.gguf) - Automatically routed to LM Studio API
• PT and IT models (e.g., google/medgemma-1.1-4b-it) - Automatically routed to Hugging Face

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🧪 API Endpoints

Health Check:

GET /health

Agentic Analysis:

POST /api/v1/orchestrator/run
# Upload image + context → get alignment verdict

GET /api/v1/orchestrator/graph
# View LangGraph workflow visualization

Individual Tools:

POST /api/v1/forensics/analyze      # Pixel forensics + EXIF analysis
POST /api/v1/reverse-search/search  # Reverse image search
POST /api/v1/ingestion/upload       # Image submission

Full API Docs: http://localhost:8000/docs (when running)

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📁 Project Structure

medcontext/
├── START_HERE.md                   ← Judge navigation guide
├── README.md                       ← This file
├── docs/
│   ├── EXECUTIVE_SUMMARY.md        ← 1-page pitch
│   ├── VALIDATION.md               ← Empirical evidence
│   ├── SUBMISSION.md               ← Comprehensive submission
│   └── AGENTIC_WORKFLOW.md          ← Technical details
├── src/app/
│   ├── orchestrator/               ← Agentic workflow
│   ├── forensics/                  ← Forensics layer
│   ├── provenance/                 ← C2PA + hash chain + optional Polygon anchoring
│   ├── reverse_search/             ← Google Vision API integration
│   ├── metrics/                    ← Integrity scoring
│   └── api/v1/endpoints/           ← REST API
├── ui/                             ← React frontend
├── tests/                          ← 62 passing tests
└── scripts/                        ← Utilities

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💡 Key Features

Agentic Orchestration

• Dynamic tool selection - Only runs necessary checks
• Prompt injection protection - Secure against adversarial inputs
• Multi-modal synthesis - Combines evidence intelligently
• Explainable verdicts - Full rationale provided

Forensics as Supporting Evidence

• Layer 1: Format-routed pixel forensics (DICOM → header integrity + copy-move; PNG/JPEG → copy-move)
• Layer 3: EXIF metadata extraction (software tags, timestamps)
• Ensemble voting: Confidence-weighted signals
• Honest framing: Supporting evidence, not definitive claims

Provenance Tracking

• C2PA manifest reading - Verifies embedded Content Provenance and Authenticity signatures
• Hash-chained records - SHA-256 linked observation blocks forming an immutable audit trail
• Polygon blockchain anchoring - Optional on-chain timestamps for independent verification
• Observation-based - Extensible chain recording submissions, manifests, and validations

Real-Time Monitoring

• Reddit integration (PRAW)
• Telegram bot (field deployment ready)

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👤 Contact & Support

Developer: Jamie Forrest GitHub: @drjforrest Website: drjforrest.com Email: forrest.jamie@gmail.com Affiliation: Scientific Director, HERO Lab, School of Nursing, University of British Columbia Partner: Counterforce AI

Questions?

• Setup issues: See Quick Start section above
• Technical details: AGENTIC_WORKFLOW.md
• Competition submission: SUBMISSION.md

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📜 License

CC-BY-4.0 License - See LICENSE file for details

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Acknowledgments

• HERO Lab - Health Equity & Resilience Observatory, UBC
• Google - MedGemma medical LLM
• Open Source Community - FastAPI, React, LangGraph, and all dependencies

Dataset Citations

Our validation dataset (160 image-claim pairs) was constructed from two publicly available medical image tampering datasets. We gratefully acknowledge the authors:

UCI Deepfakes: Medical Image Tamper Detection

Mirsky, Y., Mahler, T., Shelef, I., & Elovici, Y. (2019). CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning. USENIX Security Symposium.

• Repository: https://archive.ics.uci.edu/dataset/520/deepfakes+medical+image+tamper+detection
• DOI: 10.24432/C5J318
• License: CC BY 4.0

BTD: Back-in-Time Diffusion MRI and CT Deepfake Test Sets

Graboski, F., Mirsky, Y. (2024). Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes. ACM Transactions on Intelligent Systems and Technology.

• Paper: arXiv:2407.15169
• Dataset: https://www.kaggle.com/datasets/freddiegraboski/btd-mri-and-ct-deepfake-test-sets
• Code: https://github.com/FreddieMG/BTD--Unsupervised-Detection-of-Medical-Deepfakes
• License: AGPL-3.0

Our derived validation dataset (PoJ experiments) uses 120 authentic MRI image-claim pairs from the BTD dataset and 40 tampered medical scans from the UCI dataset, with synthetically assigned contextual labels (veracity, alignment) programmatically generated across five clinical categories for controlled hypothesis testing. These ground truth labels are not human fact-checked or expert-annotated.

For final system validation on real-world misinformation, we used the Med-MMHL benchmark (separate dataset, n=163) which contains human-annotated, expert fact-checked labels from professional fact-checking organizations.

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MedContext: The First Agentic AI System Built for Real-World Medical Misinformation

Not by detecting fake pixels, but by understanding context and meaning.

🏥 Evidence-Based • 🤖 Production-Ready • 🌍 Deployment-Ready