README.md

metadata

title: Echo AI Image Detector
emoji: 🔎
colorFrom: indigo
colorTo: purple
sdk: gradio
app_file: app.py
python_version: '3.10'
pinned: true
models:
  - OwensLab/commfor-model-384

Echo self-hosted AI image detector

Private inference service for Echo's public-image provenance pipeline. It runs two independent, open-source classifiers and never calls a commercial detection API:

• Community Forensics OwensLab/commfor-model-384 (MIT, official 21.8M-parameter checkpoint).
• GRIP-UNINA clipdet_latent10k_plus (Apache-2.0, official CLIP-based checkpoint).

The service deliberately returns both raw model results. It does not decide Echo's label; Echo's calibrated database ensemble owns that decision.

The same source can run as a free Hugging Face CPU Space. The application starts its authenticated FastAPI server directly; the gradio Space SDK is used only as the free Python hosting runtime. Pinned upstream source archives and the small GRIP classifier head are bootstrapped and verified at startup.

Run locally

cd services/ai-image-detector
export DETECTOR_AUTH_TOKEN="$(openssl rand -hex 32)"
docker compose up --build

The first start downloads the Community Forensics and CLIP backbone weights into the named model cache. Keep that volume between deployments.

curl -H "Authorization: Bearer $DETECTOR_AUTH_TOKEN" http://127.0.0.1:8091/health
curl -H "Authorization: Bearer $DETECTOR_AUTH_TOKEN" -F file=@sample.jpg http://127.0.0.1:8091/v1/detect

Configure the Supabase Edge Function with:

supabase secrets set \
  SELF_HOSTED_AI_DETECTOR_URL=https://detector.internal.example \
  SELF_HOSTED_AI_DETECTOR_TOKEN="$DETECTOR_AUTH_TOKEN"

The URL must be HTTPS in production. Keep the service private when the hosting platform supports private networking; bearer authentication remains required.

GPU build

The default image installs CPU-only PyTorch. For NVIDIA inference, build with a CUDA wheel index compatible with the host driver, for example:

docker build \
  --build-arg TORCH_INDEX_URL=https://download.pytorch.org/whl/cu124 \
  -t echo-ai-image-detector:gpu .

Run with the NVIDIA container runtime and set DETECTOR_DEVICE=cuda. CPU mode is useful for smoke tests and small review batches; the full public corpus should use a temporary GPU worker.

Operational guarantees

• Maximum upload is 15 MB by default.
• Only image bytes supplied by Echo are processed; the service does not fetch arbitrary URLs.
• One inference request runs at a time per container to avoid GPU-memory spikes.
• Both models must load successfully or /v1/detect returns 503.
• Model source revisions are pinned in the Dockerfile.
• Model disagreement remains unknown until Echo's calibration/review layer resolves it.

These classifiers estimate likelihood. They do not prove origin, and their raw probabilities must not be presented as calibrated confidence.