Detects fake voices in YouTube videos with 94% accuracy and alerts the user to prevent misinformation.
Tobias Carryer - Did the machine learning stuff
Shannon Hogan - Made the Python server (and Helped with Chrome Extension)
John Lee - Also made the Python server
Kyle Meade - Made the Chrome extension
We use multiple 1D convolutional neural network layers followed by two fully connected layers and a third fully connected output layer. We implement the model architecture with Keras in model_architecture.py. Take a look at pipeline.py to see how we train the model. Our approach achieves 94% binary classification accuracy on the ASVspoof 2019 real PA dataset, making it excellent at detecting most synthesised voices.