FaceForensics++: Learning to Detect Manipulated Facial Images
Andreas Rössler1     Davide Cozzolino2     Luisa Verdoliva2     Christian Riess3     Justus Thies1     Matthias Nießner1    
    1Technical University of Munich     2University Federico II of Naples     3University of Erlangen-Nuremberg
IEEE International Conference on Computer Vision (ICCV 2019)
Abstract

The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns on the implication on the society. At best, this leads to a loss of trust in digital content, but it might even cause further harm by spreading false information and the creation of fake news. In this paper, we examine the realism of state-of-the-art image manipulations, and how difficult it is to detect them - either automatically or by humans. In particular, we focus on DeepFakes, Face2Face, and FaceSwap as prominent representatives for facial manipulations. We create more than half a million manipulated images respectively for each approach. The resulting publicly available dataset is at least an order of magnitude larger than comparable alternatives and it enables us to train data-driven forgery detectors in a supervised fashion. We show that the use of additional domain specific knowledge improves forgery detection to an unprecedented accuracy, even in the presence of strong compression. By conducting a series of thorough experiments, we quantify the differences between classical approaches, novel deep learning approaches, and the performance of human observers.

Dataset Access

If you would like to download the FaceForensics and FaceForensics++ datasets, please fill out this google form and, once accepted, we will send you the link to our download script.

Benchmark

We are offering an automated benchmark for facial manipulation detection on the presenece of compression based on our manipulation methods. If you are interested to test your approach on unseen data, visit it here.

Source Code & Contact

For more information about our code, visit our github or contact us under faceforensics@googlegroups.com.