  Recently, deep learning-based models have exhibited remarkable performance
for image manipulation detection. However, most of them suffer from poor
universality of handcrafted or predetermined features. Meanwhile, they only
focus on manipulation localization and overlook manipulation classification. To
address these issues, we propose a coarse-to-fine architecture named
Constrained R-CNN for complete and accurate image forensics. First, the
learnable manipulation feature extractor learns a unified feature
representation directly from data. Second, the attention region proposal
network effectively discriminates manipulated regions for the next manipulation
classification and coarse localization. Then, the skip structure fuses
low-level and high-level information to refine the global manipulation
features. Finally, the coarse localization information guides the model to
further learn the finer local features and segment out the tampered region.
Experimental results show that our model achieves state-of-the-art performance.
Especially, the F1 score is increased by 28.4%, 73.2%, 13.3% on the NIST16,
COVERAGE, and Columbia dataset.
