  The recently developed deep algorithms achieve promising progress in the
field of image copy-move forgery detection (CMFD). However, they have limited
generalizability in some practical scenarios, where the copy-move objects may
not appear in the training images or cloned regions are from the background. To
address the above issues, in this work, we propose a novel end-to-end CMFD
framework by integrating merits from both conventional and deep methods.
Specifically, we design a deep cross-scale patchmatch method tailored for CMFD
to localize copy-move regions. In contrast to existing deep models, our scheme
aims to seek explicit and reliable point-to-point matching between source and
target regions using features extracted from high-resolution scales. Further,
we develop a manipulation region location branch for source/target separation.
The proposed CMFD framework is completely differentiable and can be trained in
an end-to-end manner. Extensive experimental results demonstrate the high
generalizability of our method to different copy-move contents, and the
proposed scheme achieves significantly better performance than existing
approaches.
