  With the development of deep learning technology, various forgery methods
emerge endlessly. Meanwhile, methods to detect these fake videos have also
achieved excellent performance on some datasets. However, these methods suffer
from poor generalization to unknown videos and are inefficient for new forgery
methods. To address this challenging problem, we propose UVL, a novel unified
video tampering localization framework for synthesizing forgeries.
Specifically, UVL extracts common features of synthetic forgeries: boundary
artifacts of synthetic edges, unnatural distribution of generated pixels, and
noncorrelation between the forgery region and the original. These features are
widely present in different types of synthetic forgeries and help improve
generalization for detecting unknown videos. Extensive experiments on three
types of synthetic forgery: video inpainting, video splicing and DeepFake show
that the proposed UVL achieves state-of-the-art performance on various
benchmarks and outperforms existing methods by a large margin on cross-dataset.
