Fake News Detection using Deep Learning models in Tensorflow
Fake news is misinformation or manipulated news that is spread across the social media with an intention to damage a person, agency and organisation. Due to the dissemination of fake news, there is a need for computational methods to detect them. Fake news detection aims to help users to expose varieties of fabricated news. To achieve this goal, first we have taken the datasets which contains both fake and real news and conducted various experiments to organize fake news detector. We used natural processing, machine learning and deep learning techniques to classify the datasets. We yielded a comprehensive audit of detecting fake news by including fake news categorization, existing algorithms from machine learning techniques. In this project, we explored different machine learning models like Naïve Bayes, K nearest neighbors, decision tree, random forest and deep learning networks like Shallow Convolutional Neural Networks (CNN), Deep Convolutional Neural Network (VDCNN), Long Short-Term Memory Network (LSTM), Gated Recurrent Unit Network (GRU), Combination of Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network with Gated Recurrent Unit (CNN-LSTM).
Python script to generate fake datasets optimized for testing machine learning/deep learning workflows
DeepFaceLab is the leading software for creating deepfakes.
Style transfer, deep learning, feature transform
Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
For "Deep Learning class" at ETHZ. Evaluate how well the fake voice of Barack Obama 1. confuses the voice verification system, 2. can be detected. The report of this project is available at ->
Doing research to see where we currently are with faking voice audio with neural networks/deep learning using a Deep Convolutional Text to Speech (DCTTS) model to produce pretty darn good results.