With recent advances in computer vision and graphics, it is now possible to
generate videos with extremely realistic synthetic faces, even in real time.
Countless applications are possible, some of which raise a legitimate alarm,
calling for reliable detectors of fake videos. In fact, distinguishing between
original and manipulated video can be a challenge for humans and computers
alike, especially when the videos are compressed or have low resolution, as it
often happens on social networks. Research on the detection of face
manipulations has been seriously hampered by the lack of adequate datasets. To
this end, we introduce a novel face manipulation dataset of about half a
million edited images (from over 1000 videos). The manipulations have been
generated with a state-of-the-art face editing approach. It exceeds all
existing video manipulation datasets by at least an order of magnitude. Using
our new dataset, we introduce benchmarks for classical image forensic tasks,
including classification and segmentation, considering videos compressed at
various quality levels. In addition, we introduce a benchmark evaluation for
creating indistinguishable forgeries with known ground truth; for instance with
generative refinement models.

通过引入新的半百万级别的面部操作数据集，本文提出了对诸如经典图像取证分类、分割、以及众多优化方法进行基准测试、鉴定的处理任务，包括压缩不同质量级别下的视频，并达到超越现有所有视频操纵数据集一个数量级的状态，旨在通过根据目标制作难以区分的赝品来进行基准评估。