Deep Fake Video Detection Using Recurrent Neural Networks
Keywords:
Deep Fake, Deep Learning, Face Manipulation, Convolutional- LSTM, Fake videosAbstract
Generative adversarial networks progressed to the point where it is very difficult to distinguish the difference between what is real or fake. In recent times, different face manipulating tools are used to generate credible face swaps in videos that leave a very little trace of manipulation, which is commonly referred as “AI based Deep Fake videos”. Nowadays, these realistic fake videos are used in pornography, blackmailing, political distress etc. Creation of deep fake videos are a simple task, but when it comes to detection, it’s a major challenge. The advancement in the creation of AI based deep fake videos has made the older detection system less accurate. In this work, we describe a new method which use deep learning based methodology to effectively detect manipulated/fake videos from real videos. Our system uses a combination of CNN and LSTM to detect these manipulations. Convolutional Neural Networks (CNN) is used to extract frame level features and then these features are trained using a Long Short Term Memory (LSTM) Recurrent Neural Network that classifies real and fake videos separately. We also compared the results with existing methodologies and found out to be great. Dataset for training Deep Fake detection model were picked from different sources which include DFDC, Face Forensics, Celeb DF, deep fake detection challenge dataset to name a few. We are successful in obtaining a competitive result of 92 percent accuracy while using a simple architecture.
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