  It is becoming increasingly easy to automatically replace a face of one
person in a video with the face of another person by using a pre-trained
generative adversarial network (GAN). Recent public scandals, e.g., the faces
of celebrities being swapped onto pornographic videos, call for automated ways
to detect these Deepfake videos. To help developing such methods, in this
paper, we present the first publicly available set of Deepfake videos generated
from videos of VidTIMIT database. We used open source software based on GANs to
create the Deepfakes, and we emphasize that training and blending parameters
can significantly impact the quality of the resulted videos. To demonstrate
this impact, we generated videos with low and high visual quality (320 videos
each) using differently tuned parameter sets. We showed that the state of the
art face recognition systems based on VGG and Facenet neural networks are
vulnerable to Deepfake videos, with 85.62% and 95.00% false acceptance rates
respectively, which means methods for detecting Deepfake videos are necessary.
By considering several baseline approaches, we found that audio-visual approach
based on lip-sync inconsistency detection was not able to distinguish Deepfake
videos. The best performing method, which is based on visual quality metrics
and is often used in presentation attack detection domain, resulted in 8.97%
equal error rate on high quality Deepfakes. Our experiments demonstrate that
GAN-generated Deepfake videos are challenging for both face recognition systems
and existing detection methods, and the further development of face swapping
technology will make it even more so.
