  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.

Computer Vision and Pattern Recognition