  With the rapid advances of image editing techniques in recent years, image
manipulation detection has attracted considerable attention since the
increasing security risks posed by tampered images. To address these
challenges, a novel multi-scale multi-grained deep network (MSMG-Net) is
proposed to automatically identify manipulated regions. In our MSMG-Net, a
parallel multi-scale feature extraction structure is used to extract
multi-scale features. Then the multi-grained feature learning is utilized to
perceive object-level semantics relation of multi-scale features by introducing
the shunted self-attention. To fuse multi-scale multi-grained features, global
and local feature fusion block are designed for manipulated region segmentation
by a bottom-up approach and multi-level feature aggregation block is designed
for edge artifacts detection by a top-down approach. Thus, MSMG-Net can
effectively perceive the object-level semantics and encode the edge artifact.
Experimental results on five benchmark datasets justify the superior
performance of the proposed method, outperforming state-of-the-art manipulation
detection and localization methods. Extensive ablation experiments and feature
visualization demonstrate the multi-scale multi-grained learning can present
effective visual representations of manipulated regions. In addition, MSMG-Net
shows better robustness when various post-processing methods further manipulate
images.
