A newer version of the Gradio SDK is available:
5.34.1
title: MCP Hackathon Deepfake Watchdog
emoji: 🛡️
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: false
license: mit
short_description: Upload your image and/or voice to scan for deepfake misuse o
🛡️ Deepfake Watchdog 🤖
🏷️ Tags: mcp-server-track agent-demo-track
A multimodal AI agent that detects deepfake content using face, voice, and video verification. Built with 🧠 DeepFace, 🗣️ SpeechBrain, 🎥 OpenCV, and accelerated with ☁️ Modal for serverless GPU-powered execution. Designed for the MCP HuggingFace's Hackathon.
🚀 Features
- 🧑🦱 Face Verification: Compare two faces and detect if they belong to the same person using DeepFace.
- 🎙️ Voice Verification: Determine if two audio samples are from the same speaker with SpeechBrain.
- 🎞️ Video Scan for Deepfakes: Scan a video (or YouTube link) and match all faces against a reference image.
- 📄 PDF Report: Generate a downloadable report with all scan results.
- 🧠 Runs as an MCP Agent: Compatible with Gradio MCP, fully pluggable into agentic workflows.
- ⚡ Modal Offloading: GPU-backed execution for DeepFace & SpeechBrain using Modal.
🧱 Architecture
Gradio UI ──> MCP Agent ──> Modal Functions └─> verify_faces_remote() └─> verify_voices_remote() └─> verify_faces_in_video_remote()
🧩 Tech Stack
| Tool | Purpose |
|---|---|
Gradio |
Frontend UI + MCP integration |
Modal |
GPU offloading for ML workloads |
DeepFace |
Face verification |
SpeechBrain |
Voice verification |
OpenCV |
Video processing |
Pillow |
Image manipulation |
TensorFlow |
Required by DeepFace |
FFmpeg |
(Optional) for YouTube downloads |
🛠️ Setup Instructions
1. Clone the repo
git clone https://huggingface.co/spaces/abetavarez/MCP-Hackathon-Deepfake-Watchdog
cd MCP-Hackathon-Deepfake-Watchdog
2. Create .env
cp .env.example .env
# Add your API keys or configs if required
3. Install dependencies (locally)
pip install -r requirements.txt
4. Run the app (with MCP)
modal run app.py
or for development::
python app.py
⚙️ Modal Setup
The app uses a single modal_app/backend.py file that defines:
verify_faces_remote
verify_voices_remote
verify_faces_in_video_remote
Each function is GPU-accelerated and runs independently on demand.
modal deploy modal_app.py
🧠 Agent MCP Integration
This app runs as an MCP server using:
demo.launch(mcp_server=True)
You can use this tool in your LlamaIndex, Hugging Face or LangChain agent as a remote service.
🏆 Hackathon Tracks ✅ Track 1: MCP Tool — Turned into a reusable tool with mcp_server=True ✅ Track 3: Agentic Demo — A fully capable AI agent for deepfake detection (LlamaIndex) ✅ Modal — Offloaded heavy tasks to Modal’s GPU cloud
🧱 Folder Structure
. ├── app.py # Main app entry point (Gradio + MCP) ├── llama_agent.py # LlamaIndex Agent implementation ├── tools.py # Tools for LlamaIndex Agent and MCP ├── modal_app/ │ ├── backend.py # Modal remote functions ├── detector/ │ ├── face.py # Local face verification logic │ ├── voice.py # Local voice verification logic │ └── video.py # Local video scan logic ├── reports/ │ └── pdf_report.py # PDF report generation ├── utils/ │ └── youtube_utils.py # YouTube download helper ├── README.md ├── requirements.txt └── .env.example
##🧑💻 Author Abraham Efren Tavarez GitHub - @AbeTavarez LinkedIn — @abrahametavarez
✨ Future Improvements
Add S3-compatible cloud storage or video ingest
Add Nebius S3 support for video uploads
Add RAG-based evidence summarizer
Enable auto-scan via background agent
Add QR code for mobile voice capture