Detecting maliciously falsified facial images and videos has attracted extensive attention from digital-forensics and computer-vision communities. An important topic in manipulation detection is the localization of the fake regions. Previous work related to forgery detection mostly focuses on the entire faces. However, recent forgery methods have developed to edit important facial components while maintaining others unchanged. This drives us to not only focus on the forgery detection but also fine-grained falsified region segmentation. In this paper, we propose a collaborative feature learning approach to simultaneously detect manipulation and segment the falsified components. With the collaborative manner, detection and segmentation can boost each other efficiently. To enable our study of forgery detection and segmentation, we build a facial forgery dataset consisting of both entire and partial face forgeries with their pixel-level manipulation ground-truth. Experiment results have justified the mutual promotion between forgery detection and manipulated region segmentation. The overall performance of the proposed approach is better than the state-of-the-art detection or segmentation approaches. The visualization results have shown that our proposed model always captures the artifacts on facial regions, which is more reasonable.

翻译：检测恶意伪造的人脸图像和视频已引起数字取证与计算机视觉领域的广泛关注。在篡改检测中，伪造区域的定位是一个重要课题。以往的伪造检测研究大多集中于整张人脸。然而，近期伪造方法已发展为在保持其他部分不变的同时仅编辑重要面部组件。这促使我们不仅关注伪造检测，还需进行细粒度伪造区域分割。本文提出一种协同特征学习方法，可同时实现篡改检测与伪造组件分割。通过协同机制，检测与分割任务可相互促进提升。为支持伪造检测与分割研究，我们构建了一个包含整脸及局部人脸伪造数据的数据集，并提供像素级篡改标注。实验结果表明，伪造检测与篡改区域分割之间存在相互促进作用。所提方法在检测与分割任务上的整体性能均优于当前最优方法。可视化结果显示，所提模型总能捕获面部区域的篡改伪影，具有更高的合理性。