reverse-search

Reverse Search Project

This repository contains the implementation of the reverse-search pipeline for image deepfake detection.

Watch an overview of the Machine Learning team discussing this reverse search detector:

Project Structure

• Dockerfile: Defines the Docker image for the application.
• classify.py: Script for image classification.
• docker_run.sh: Shell script to run the Docker container.
• requirements.txt: Python dependencies.
• server.py: Main sonic server application.

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Prerequisites

1. Docker: Ensure Docker is installed and running on your system.
2. API Keys: Obtain the necessary API keys. You will need 3 API keys: PPLX_API_KEY (for perplexity), OPENAI_API_KEY (for GPT-4), and GOOGLE_APPLICATION_CREDENTIALS (for running reverse image search)

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Step-by-Step Guide

1. Build the Docker Image

1. Build the Docker image using the Dockerfile:

   docker build -t reverse_search_image:<tag> -f Dockerfile .

3. Run the Docker Container

1. Run the docker container with the following command:

   docker run  -d \
            -e PPLX_API_KEY=<your_pplx_api_key> \
            -e OPENAI_API_KEY=<your_openai_api_key> \
            -p 8000:8000 \
            -e SERVER_PORT=8000 \
            -e GOOGLE_APPLICATION_CREDENTIALS="{<your_goole_application_credentials>}" \
            --name "reverse-search" "$DOCKER_REGISTRY/reverse-search:<tag>"

   Replace <your_pplx_api_key>, <your_openai_api_key>, and <your_goole_application_credentials> with the required API keys.

   The google application credentials should look like the following:

   '{"type":"service_account","project_id":"...<id>...","private_key_id":"...<id>...","private_key":"...<key>...","client_email":"...<email>...","client_id":"...<id>...","auth_uri":"https://accounts.google.com/o/oauth2/auth","token_uri":"https://oauth2.googleapis.com/token","auth_provider_x509_cert_url":"https://www.googleapis.com/oauth2/v1/certs","client_x509_cert_url":"...<url>...","universe_domain":"googleapis.com"}'
   

2. Verify the container is running:

   docker ps

4. Test the Application

1. In a separate terminal, run the following command one or more times

curl -X GET http://localhost:8000/healthcheck

until you see {"healthy":true}.

2. Then, test that inference can be run as expected:

curl -X POST http://localhost:8000/predict \
     -H "Content-Type: application/json" \
     --data '{"file_path":"https://uploads.civai.org/files/jhxTVhsg/b751515306e7.jpg"}'

3. Finally, if successful, push the docker image to docker hub:

docker login

docker push "$DOCKER_REGISTRY/reverse_search_image:<tag>"

Running in GCP

0. Login to your instance. in this case, it is <PLACEHOLDER_IP>
1. Pull the Docker Container

docker pull "$DOCKER_REGISTRY/reverse_search_image:<tag>"

For our use case,

docker pull kevintm1/reverse-search:v1.0

1. Run the docker container, as described in Step 3: Run the Docker Container.

2. Test the model in a separate terminal using:

curl -X GET http://<PLACEHOLDER_IP>:80/healthcheck

and

curl  -X POST http://<PLACEHOLDER_IP>:80/predict \
      -H "Content-Type: application/json"
      --data '{"file_path":"https://uploads.civai.org/files/jhxTVhsg/b751515306e7.jpg"}'

The expected output should be in the format

{"score":0,"rationale":"The image appears ......","sourceUrl":"..."}