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.

通过提取合成伪造物的共同特征，如合成边缘的边界伪影、生成像素的不自然分布以及伪造区域与原始区域之间的非相关性，我们提出了一种新颖的统一视频篡改定位框架UVL，它在检测未知视频方面具有优异的表现，并且对于新的伪造方法非常高效。通过对三种合成伪造类型进行的广泛实验，包括视频修复、视频剪辑和DeepFake，表明所提出的UVL在各种基准测试中取得了领先的性能，并在跨数据集上明显优于现有方法。

UVL: 一个统一的视频篡改定位框架