Complement Face Forensic Detection and Localization with FacialLandmarks
This addresses the challenge of verifying digital media authenticity for security and forensic applications, but it is incremental as it builds on existing face forensic detection methods.
The authors tackled the problem of detecting and localizing manipulated face images by creating a new dataset with 1.3 million images and proposing a method that uses facial landmarks to improve performance, achieving state-of-the-art results on their dataset and FaceForensics++.
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.