  In recent years, the abuse of a face swap technique called deepfake Deepfake
has raised enormous public concerns. So far, a large number of deepfake videos
(known as "deepfakes") have been crafted and uploaded to the internet, calling
for effective countermeasures. One promising countermeasure against deepfakes
is deepfake detection. Several deepfake datasets have been released to support
the training and testing of deepfake detectors, such as DeepfakeDetection and
FaceForensics++. While this has greatly advanced deepfake detection, most of
the real videos in these datasets are filmed with a few volunteer actors in
limited scenes, and the fake videos are crafted by researchers using a few
popular deepfake softwares. Detectors developed on these datasets may become
less effective against real-world deepfakes on the internet. To better support
detection against real-world deepfakes, in this paper, we introduce a new
dataset WildDeepfake, which consists of 7,314 face sequences extracted from 707
deepfake videos collected completely from the internet. WildDeepfake is a small
dataset that can be used, in addition to existing datasets, to develop and test
the effectiveness of deepfake detectors against real-world deepfakes. We
conduct a systematic evaluation of a set of baseline detection networks on both
existing and our WildDeepfake datasets, and show that WildDeepfake is indeed a
more challenging dataset, where the detection performance can decrease
drastically. We also propose two (eg. 2D and 3D) Attention-based Deepfake
Detection Networks (ADDNets) to leverage the attention masks on real/fake faces
for improved detection. We empirically verify the effectiveness of ADDNets on
both existing datasets and WildDeepfake. The dataset is available
at:https://github.com/deepfakeinthewild/deepfake-in-the-wild.
