  Unlike ordinary computer vision tasks that focus more on the semantic content
of images, the image manipulation detection task pays more attention to the
subtle information of image manipulation. In this paper, the noise image
extracted by the improved constrained convolution is used as the input of the
model instead of the original image to obtain more subtle traces of
manipulation. Meanwhile, the dual-branch network, consisting of a
high-resolution branch and a context branch, is used to capture the traces of
artifacts as much as possible. In general, most manipulation leaves
manipulation artifacts on the manipulation edge. A specially designed
manipulation edge detection module is constructed based on the dual-branch
network to identify these artifacts better. The correlation between pixels in
an image is closely related to their distance. The farther the two pixels are,
the weaker the correlation. We add a distance factor to the self-attention
module to better describe the correlation between pixels. Experimental results
on four publicly available image manipulation datasets demonstrate the
effectiveness of our model.
