Recently, Generative Adversarial Networks (GANs) and image manipulating
methods are becoming more powerful and can produce highly realistic face images
beyond human recognition which have raised significant concerns regarding the
authenticity of digital media. Although there have been some prior works that
tackle face forensic classification problem, it is not trivial to estimate
edited locations from classification predictions. In this paper, we propose, to
the best of our knowledge, the first rigorous face forensic localization
dataset, which consists of genuine, generated, and manipulated face images. In
particular, the pristine parts contain face images from CelebA and FFHQ
datasets. The fake images are generated from various GANs methods, namely
DCGANs, LSGANs, BEGANs, WGAN-GP, ProGANs, and StyleGANs. Lastly, the edited
subset is generated from StarGAN and SEFCGAN based on free-form masks. In
total, the dataset contains about 1.3 million facial images labelled with
corresponding binary masks.
Based on the proposed dataset, we demonstrated that explicit adding facial
landmarks information in addition to input images improves the performance. In
addition, our proposed method consists of two branches and can coherently
predict face forensic detection and localization to outperform the previous
state-of-the-art techniques on the newly proposed dataset as well as the
faceforecsic++ dataset especially on low-quality videos.

本文提出了一个包含真实照片、生成照片和编辑照片的面部取证定位数据集，并演示了通过在输入图像中显式添加面部标志信息，提高面部索证检测和定位性能的方法。