  The proliferation of deepfake media is raising concerns among the public and
relevant authorities. It has become essential to develop countermeasures
against forged faces in social media. This paper presents a comprehensive study
on two new countermeasure tasks: multi-face forgery detection and segmentation
in-the-wild. Localizing forged faces among multiple human faces in unrestricted
natural scenes is far more challenging than the traditional deepfake
recognition task. To promote these new tasks, we have created the first
large-scale dataset posing a high level of challenges that is designed with
face-wise rich annotations explicitly for face forgery detection and
segmentation, namely OpenForensics. With its rich annotations, our
OpenForensics dataset has great potentials for research in both deepfake
prevention and general human face detection. We have also developed a suite of
benchmarks for these tasks by conducting an extensive evaluation of
state-of-the-art instance detection and segmentation methods on our newly
constructed dataset in various scenarios. The dataset, benchmark results,
codes, and supplementary materials will be publicly available on our project
page: https://sites.google.com/view/ltnghia/research/openforensics
