  Constrained image splicing detection and localization (CISDL) is a newly
proposed challenging task for image forensics, which investigates two input
suspected images and identifies whether one image has suspected regions pasted
from the other. In this paper, we propose a novel adversarial learning
framework to train the deep matching network for CISDL. Our framework mainly
consists of three building blocks: 1) the deep matching network based on atrous
convolution (DMAC) aims to generate two high-quality candidate masks which
indicate the suspected regions of the two input images, 2) the detection
network is designed to rectify inconsistencies between the two corresponding
candidate masks, 3) the discriminative network drives the DMAC network to
produce masks that are hard to distinguish from ground-truth ones. In DMAC,
atrous convolution is adopted to extract features with rich spatial
information, the correlation layer based on the skip architecture is proposed
to capture hierarchical features, and atrous spatial pyramid pooling is
constructed to localize tampered regions at multiple scales. The detection
network and the discriminative network act as the losses with auxiliary
parameters to supervise the training of DMAC in an adversarial way. Extensive
experiments, conducted on 21 generated testing sets and two public datasets,
demonstrate the effectiveness of the proposed framework and the superior
performance of DMAC.
