  The technological advancements of deep learning have enabled sophisticated
face manipulation schemes, raising severe trust issues and security concerns in
modern society. Generally speaking, detecting manipulated faces and locating
the potentially altered regions are challenging tasks. Herein, we propose a
conceptually simple but effective method to efficiently detect forged faces in
an image while simultaneously locating the manipulated regions. The proposed
scheme relies on a segmentation map that delivers meaningful high-level
semantic information clues about the image. Furthermore, a noise map is
estimated, playing a complementary role in capturing low-level clues and
subsequently empowering decision-making. Finally, the features from these two
modules are combined to distinguish fake faces. Extensive experiments show that
the proposed model achieves state-of-the-art detection accuracy and remarkable
localization performance.
