Deepfake Detection
A new deep learning-based method that can effectively distinguish AI-generated fake videos from real videos is described, capable of automatically detecting the replacement and reenactment deep fakes.
Abstract
: The growing computation power has made the deep learning algorithms so powerful that creating an indistinguishable human synthesized video popularly called as deepfakes have become very simple. Scenarios where this realistic face swapped deepfakes are used to create political distress, fake terrorism events, revenge porn, blackmail peoples are easily envisioned. In this work, we describe a new deep learning-based method that can effectively distinguish AI-generated fake videos from real videos. Our method is capable of automatically detecting the replacement and reenactment deep fakes. We are trying to use Artificial Intelligence (AI) to fight Artificial Intelligence (AI). Our system uses a Res-Next Convolution neural network to extract the frame-level features and these features and further used to train the Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) to classify whether the video is subject to any kind of manipulation or not, i.e., whether the video is deep fake or real video. To emulate the real time scenarios and make the model perform better on real time data, we evaluate our method on large amount of stable and mixed dataset prepared by mixing the various available dataset like Face-Forensic++ , Deepfake detection challenge , and Celeb-DF. We also show how our system can achieve competitive result using very simple and robust approach .