  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.
