Exposing DeepFakes Using Convolutional Neural Networks and Transfer Learning Approaches

Shraddha Suratkar, Faruk Kazi, Mukul Sakhalkar, Nikhil Abhyankar, Mrunal Kshirsagar

Abstract: Advancements in Artificial Intelligence - oriented computing power and the ever-growing reach of social media have proven to be catalysts in emergence and spread of a new vein of AI generated fake videos known as 'DeepFake' videos. Such videos are synthesized using generative machine learning models like Generative Adversarial Networks or Variational AutoEncoders and they can achieve high degrees of realism. Spread of sensitive political or obscene content in form of such videos may lead to social distress to the target entity(s). This paper presents a study pertinent to the detection of DeepFake videos using Convolutional Neural Networks (CNNs) with transfer learning. A comparative study of the performance of various models in the detection of tampered videos has been presented. These models are trained (fine-tuned) and tested on a custom dataset encompassing randomly selected labelled frames from videos in the DeepFake Detection Dataset by Google AI and FaceForensics++ dataset