RStudio AI Blog: Deepfake detection challenge from R


Deepfake detection challenge from R

A couple of months ago, Amazon, Facebook, Microsoft, and other contributors initiated a challenge consisting of telling apart real and AI-generated (“fake”) videos. We show how to approach this challenge from R.

Turgut Abdullayev, QSS Analytics - Aug. 18, 2020


Introduction

Working with video datasets, particularly with respect to detection of AI-based fake objects, is very challenging due to proper frame selection and face detection. To approach this challenge from R, one can make use of capabilities offered by OpenCV, magick , and keras .

Our approach consists of the following consequent steps:

  • read all the videos
  • capture and extract images from the videos
  • detect faces from the extracted images
  • crop the faces
  • build an image classification model with Keras

Let’s quickly introduce the non-deep-learning libraries we’re using. OpenCV is a computer vision library that includes:

On the other hand, magick is the open-source image-processing library that will help to read and extract useful features from video datasets:

  • Read video files
  • Extract images per second from the video
  • Crop the faces from the images

Before we go into a detailed explanation, readers should know that there is no need to copy-paste code chunks. Because at the end of the post one can find a link to Google Colab with GPU acceleration. This kernel allows everyone to run and reproduce the same results.

Read more at https://blogs.rstudio.com/ai/posts/2020-08-18-deepfake/

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