  On existing public benchmarks, face forgery detection techniques have
achieved great success. However, when used in multi-person videos, which often
contain many people active in the scene with only a small subset having been
manipulated, their performance remains far from being satisfactory. To take
face forgery detection to a new level, we construct a novel large-scale
dataset, called FFIW-10K, which comprises 10,000 high-quality forgery videos,
with an average of three human faces in each frame. The manipulation procedure
is fully automatic, controlled by a domain-adversarial quality assessment
network, making our dataset highly scalable with low human cost. In addition,
we propose a novel algorithm to tackle the task of multi-person face forgery
detection. Supervised by only video-level label, the algorithm explores
multiple instance learning and learns to automatically attend to tampered
faces. Our algorithm outperforms representative approaches for both forgery
classification and localization on FFIW-10K, and also shows high generalization
ability on existing benchmarks. We hope that our dataset and study will help
the community to explore this new field in more depth.
