Face Forensics in the Wild

• URL: http://arxiv.org/abs/2103.16076v1
• Date: Tue, 30 Mar 2021 05:06:19 GMT
• Title: Face Forensics in the Wild
• Authors: Tianfei Zhou, Wenguan Wang, Zhiyuan Liang, Jianbing Shen
• Abstract summary: We construct a novel large-scale dataset, called FFIW-10K, which comprises 10,000 high-quality forgery videos. The manipulation procedure is fully automatic, controlled by a domain-adversarial quality assessment network. In addition, we propose a novel algorithm to tackle the task of multi-person face forgery detection.
• Score: 121.23154918448618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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