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：从数字取证和计算机视觉社区，检测恶意篡改的人脸图像和视频引起了广泛关注。篡改检测中一个重要的主题是伪造区域的定位。以前与伪造检测相关的工作主要集中在整个面部。然而，最近的伪造方法已经发展到编辑重要的面部组件，同时保持其他部分不变。这促使我们不仅关注于伪造检测，而且是细粒度篡改区域的分割。在本文中，我们提出了一种协作特征学习方法，可以同时检测篡改和分割伪造部分。通过协作方式，检测和分割可以有效地互相促进。为了使我们研究伪造检测和分割，我们构建了一个包含整个和部分面部伪造及其像素级篡改标签的数据集。实验结果证明了伪造检测和篡改区域分割之间的相互促进。所提出的方法的整体性能优于最先进的检测或分割方法。可视化结果表明，我们提出的模型总是捕捉到面部区域上的痕迹，更加合理。