Forgery detection of low quality deepfake videos

Abstract

The rapid growth of online media over different social media platforms or over the internet along with many benefits have some negative effects as well. Over the past few years we are seeing that along with positive usage of Deep Learning in many areas like Medical, Animations and Cybersecurity we are seeing Deep Learning is been used for negative aspects as well for defaming or black-mailing someone creating privacy concerns for the general public. The growth in the facial forgery of a person in Media like images or videos is called DeepFakes. The advancement in the forgery creation area have challenged the researchers to create and develop advance Forgery detection systems capable to detect facial forgeries. Proposed Forgery Detection system works on the CNN-LSTM model in which we first extracted faces from the frames using MTCNN then performed Spatial Feature extraction using pretrained Xception Net and then used LSTM for Temporal Feature extraction and performed classification to predict the video as real or Fake. The system is capable of detecting low quality videos. The current system has shown good accuracy results for detecting real or fake videos on the Google Deepfake AI Dataset.