  Image editing techniques enable people to modify the content of an image
without leaving visual traces and thus may cause serious security risks. Hence
the detection and localization of these forgeries become quite necessary and
challenging. Furthermore, unlike other tasks with extensive data, there is
usually a lack of annotated forged images for training due to annotation
difficulties. In this paper, we propose a self-adversarial training strategy
and a reliable coarse-to-fine network that utilizes a self-attention mechanism
to localize forged regions in forgery images. The self-attention module is
based on a Channel-Wise High Pass Filter block (CW-HPF). CW-HPF leverages
inter-channel relationships of features and extracts noise features by high
pass filters. Based on the CW-HPF, a self-attention mechanism, called forgery
attention, is proposed to capture rich contextual dependencies of intrinsic
inconsistency extracted from tampered regions. Specifically, we append two
types of attention modules on top of CW-HPF respectively to model internal
interdependencies in spatial dimension and external dependencies among
channels. We exploit a coarse-to-fine network to enhance the noise
inconsistency between original and tampered regions. More importantly, to
address the issue of insufficient training data, we design a self-adversarial
training strategy that expands training data dynamically to achieve more robust
performance. Specifically, in each training iteration, we perform adversarial
attacks against our network to generate adversarial examples and train our
model on them. Extensive experimental results demonstrate that our proposed
algorithm steadily outperforms state-of-the-art methods by a clear margin in
different benchmark datasets.
