This is an image classification tool that uses a convolutional neural network to determine if an image is real, or has been altered or generated.

Preprocessing algorithms:

• Error-Level Analysis (ELA)
• Luminence Gradient (LG)
• Principle Component Analysis (PCA)

Neural Network:

• Very-deep convolutional neural network based on VGG19.

Fake Image Detector uses a number of open source projects to work properly:

• opencvsharp - .NET bindings for OpenCV
• Keras.NET - .NET wrapper around the Python Keras library
• .NET Core - Cross-platform runtime
• Python - Needed for ML libraries for current implementation
• TensorFlow - ML framework
• plaidML - Wrapper around keras for AMD GPU acceleration
• Keras - Deep learning API

This project requires Python 3.7 to run.

Install the dependencies:

• .NET Core 3.1 (Optional)
• Latest version of Python 3.7 (3.78)

Once python is installed, install the required modules:

$ pip install keras==2.2.4 tensorflow tensorflow-cp

For AMD hardware:

pip install keras==2.2.4 tensorflow tensorflow-cpu plaidml-keras 
plaidml-setup

For production release:

$ dotnet build

Generating release for distribution (.NET Core runtime included):

$ dotnet publish -p:PublishProfile=Full FakeDetectorUI
$ dotnet publish -p:PublishProfile=Full NeuralNetworkTraining

For a cross-platform distribution:

$ dotnet publish -p:PublishProfile=Minimal FakeDetectorUI
$ dotnet publish -p:PublishProfile=Minimal NeuralNetworkTraining

• Write more Tests
• Convert to ML.NET framework

LGPL v2.1