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Recently, Generative Adversarial Networks (GANs) and image manipulatingmethods are becoming more powerful and can produce highly realistic face imagesbeyond human recognition which have raised significant concerns regarding theauthenticity of digital media. Although there have been some prior works thattackle face forensic classification problem, it is not trivial to estimateedited locations from classification predictions. In this paper, we propose, tothe best of our knowledge, the first rigorous face forensic localizationdataset, which consists of genuine, generated, and manipulated face images. Inparticular, the pristine parts contain face images from CelebA and FFHQdatasets. The fake images are generated from various GANs methods, namelyDCGANs, LSGANs, BEGANs, WGAN-GP, ProGANs, and StyleGANs. Lastly, the editedsubset is generated from StarGAN and SEFCGAN based on free-form masks. Intotal, the dataset contains about 1.3 million facial images labelled withcorresponding binary masks.  Based on the proposed dataset, we demonstrated that explicit adding faciallandmarks information in addition to input images improves the performance. Inaddition, our proposed method consists of two branches and can coherentlypredict face forensic detection and localization to outperform the previousstate-of-the-art techniques on the newly proposed dataset as well as thefaceforecsic++ dataset especially on low-quality videos.

Complement Face Forensic Detection and Localization with FacialLandmarks