  Finding tampered regions in images is a hot research topic in machine
learning and computer vision. Although many image manipulation location
algorithms have been proposed, most of them only focus on the RGB images with
different color spaces, and the frequency information that contains the
potential tampering clues is often ignored. In this work, a novel end-to-end
two-stream boundary-aware network (abbreviated as TBNet) is proposed for
generic image manipulation localization in which the RGB stream, the frequency
stream, and the boundary artifact location are explored in a unified framework.
Specifically, we first design an adaptive frequency selection module (AFS) to
adaptively select the appropriate frequency to mine inconsistent statistics and
eliminate the interference of redundant statistics. Then, an adaptive
cross-attention fusion module (ACF) is proposed to adaptively fuse the RGB
feature and the frequency feature. Finally, the boundary artifact location
network (BAL) is designed to locate the boundary artifacts for which the
parameters are jointly updated by the outputs of the ACF, and its results are
further fed into the decoder. Thus, the parameters of the RGB stream, the
frequency stream, and the boundary artifact location network are jointly
optimized, and their latent complementary relationships are fully mined. The
results of extensive experiments performed on four public benchmarks of the
image manipulation localization task, namely, CASIA1.0, COVER, Carvalho, and
In-The-Wild, demonstrate that the proposed TBNet can significantly outperform
state-of-the-art generic image manipulation localization methods in terms of
both MCC and F1.
