Building a Deepfake Detection System using SageMaker

Deepfakes pose a significant challenge in the digital world, making the need for automated systems to detect deepfakes critical. This tutorial will guide you through building a system capable of detecting deepfakes. We will be using open-source models and AWS SageMaker.

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climb.dev simplifies all this with a package that sets up ML model deployment, hosting, versioning, tuning, and inference at scale. All without your data leaving your AWS account.

Step 1: Choose a Pretrained Model

For our deep fake detection system, we will use Mesonet. You can clone the repository with this command:

git clone https://github.com/DariusAf/MesoNet.git

Step 2: Setting Up AWS SageMaker

Create a SageMaker instance

Log into your AWS Management Console, navigate to the SageMaker console, and create a new notebook instance.

Install necessary libraries

In the Jupyter notebook, install the necessary libraries:

pip install tensorflow keras opencv-python

Import the Mesonet model

The code to import the model will look like this:

from keras.models import load_modelmodel = load_model('MesoNet.h5') # Assuming model is named as 'MesoNet.h5'

Set up IAM roles

In the AWS Management Console, navigate to IAM and create a new role. Be sure to include the necessary permissions for SageMaker to access S3 buckets.

Step 3: Deploying the Model in SageMaker

Create an endpoint configuration

In the SageMaker dashboard, navigate to the "Endpoint configurations" section and click on "Create endpoint configuration". Provide a name and the details of the model.

import sagemaker
from sagemaker import get_execution_role

sagemaker_session = sagemaker.Session()
role = get_execution_role()

model = sagemaker.tensorflow.serving.Model(model_data='s3://<bucket>/<model path>',
                                           role=role,
                                           framework_version='2.0')

Create an endpoint

Deploy the model to an endpoint.

predictor = model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')

Test the endpoint

Test your endpoint with a sample image or video.

response = predictor.predict(data)
print(response)

Step 4: Building an Inference Pipeline

Create a lambda function for preprocessing

import boto3
import json
sagemaker_runtime = boto3.client('sagemaker-runtime')

def lambda_handler(event, context):
    response = sagemaker_runtime.invoke_endpoint(
        EndpointName = '<your_endpoint_name>',
        ContentType = 'application/json',
        Body = json.dumps(event['body'])
    )
    return {
        'statusCode' : 200,
        'body' : response
    }

Create a lambda function for postprocessing

You can create a similar Lambda function for postprocessing, modifying the output to a more human-readable format.

Connect the lambda functions to your endpoint

You will need to create a REST API in Amazon API Gateway. Each Lambda function should be set as the integration response for the respective POST method.

Step 5: Future Steps

Fine-tuning

To fine-tune your model, you would first collect a labeled dataset, then use SageMaker's training functionality. Here's a basic example:

estimator = TensorFlow(entry_point='train.py',role=role,train_instance_count=1,train_instance_type='ml.p2.xlarge',framework_version='2.0',py_version='py3',script_mode=True)
estimator.fit('s3:///')

Feedback loop

Implementing a feedback loop will require a means for users to provide feedback (perhaps a button in your app), and a system for storing this feedback (like a database). You can then use this feedback as labeled data for model fine-tuning.

Update model periodically

Updating the model is as simple as repeating the steps for model deployment with the new model. You might want to automate this process, depending on how frequently your model updates.

Remember that these are example codes and need to be modified according to your specific use case and dataset. Also, make sure to handle any errors and edge cases in your code.

Step 6: Scale

Now that the ~easy~ work is done, it's time to productionize it against your existing application. If this felt overwhelming, you're in good company because it is! That's why we built Climb.dev as a one-click deployment alternative within your AWS VPC. Nothing ever leaves.