论文标题
DeeldererForensics-1.0：用于现实世界伪造检测的大型数据集
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
论文作者
论文摘要
我们介绍了构建大规模基准以进行伪造检测的持续努力。该基准的第一个版本，DeepererForensics-1.0，是迄今为止最大的面部伪造探测数据集，其中60,000个视频由总计1,760万帧构成，是同类现有数据集的10倍。采用广泛的现实扰动来获得更大规模和更高多样性的更具挑战性的基准。 DeelderForensics-1.0中的所有源视频均经过精心收集，并且虚假视频都是由新提出的端到端面部交换框架生成的。生成的视频的质量优于通过用户研究验证的现有数据集中的视频。该基准具有隐藏的测试集，其中包含在人类评估中获得高欺骗性得分的操纵视频。我们进一步贡献了一项全面的研究，该研究评估了五个代表性检测基线，并对不同的环境进行了彻底的分析。
We present our on-going effort of constructing a large-scale benchmark for face forgery detection. The first version of this benchmark, DeeperForensics-1.0, represents the largest face forgery detection dataset by far, with 60,000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. Extensive real-world perturbations are applied to obtain a more challenging benchmark of larger scale and higher diversity. All source videos in DeeperForensics-1.0 are carefully collected, and fake videos are generated by a newly proposed end-to-end face swapping framework. The quality of generated videos outperforms those in existing datasets, validated by user studies. The benchmark features a hidden test set, which contains manipulated videos achieving high deceptive scores in human evaluations. We further contribute a comprehensive study that evaluates five representative detection baselines and make a thorough analysis of different settings.
