Detecting manipulated facial images and videos is an increasingly important
topic in digital media forensics. As advanced face synthesis and manipulation
methods are made available, new types of fake face representations are being
created which have raised significant concerns for their use in social media.
Hence, it is crucial to detect manipulated face images and localize manipulated
regions. Instead of simply using multi-task learning to simultaneously detect
manipulated images and predict the manipulated mask (regions), we propose to
utilize an attention mechanism to process and improve the feature maps for the
classification task. The learned attention maps highlight the informative
regions to further improve the binary classification (genuine face v. fake
face), and also visualize the manipulated regions. To enable our study of
manipulated face detection and localization, we collect a large-scale database
that contains numerous types of facial forgeries. With this dataset, we perform
a thorough analysis of data-driven fake face detection. We show that the use of
an attention mechanism improves facial forgery detection and manipulated region
localization.

利用注意机制改善学习到的特征图，提高二分类（真实脸或伪造脸）的准确性和可视化处理后的伪造部位，通过大规模数据库的实验，证明了注意机制在人脸伪造检测和定位的有效性。