Can Deep Network Balance Copy-Move Forgery Detection and
  Distinguishment?
Shizhen Chang
Copy-move forgery detection is a crucial research area within digital image
forensics, as it focuses on identifying instances where objects in an image are
duplicated and placed in different locations. The detection of such forgeries
is particularly important in contexts where they can be exploited for malicious
purposes. Recent years have witnessed an increased interest in distinguishing
between the original and duplicated objects in copy-move forgeries, accompanied
by the development of larger-scale datasets to facilitate this task. However,
existing approaches to copy-move forgery detection and source/target
differentiation often involve two separate steps or the design of individual
end-to-end networks for each task. In this paper, we propose an innovative
method that employs the transformer architecture in an end-to-end deep neural
network. Our method aims to detect instances of copy-move forgery while
simultaneously localizing the source and target regions. By utilizing this
approach, we address the challenges posed by multi-object copy-move scenarios
and report if there is a balance between the detection and differentiation
tasks. To evaluate the performance of our proposed network, we conducted
experiments on two publicly available copy-move datasets. The results and
analysis aims to show the potential significance of our focus in balancing
detection and distinguishment result and transferring the trained model in
different datasets in the field.