  Copy-move forgery detection identifies a tampered image by detecting pasted
and source regions in the same image. In this paper, we propose a novel
two-stage framework specially for copy-move forgery detection. The first stage
is a backbone self deep matching network, and the second stage is named as
Proposal SuperGlue. In the first stage, atrous convolution and skip matching
are incorporated to enrich spatial information and leverage hierarchical
features. Spatial attention is built on self-correlation to reinforce the
ability to find appearance similar regions. In the second stage, Proposal
SuperGlue is proposed to remove false-alarmed regions and remedy incomplete
regions. Specifically, a proposal selection strategy is designed to enclose
highly suspected regions based on proposal generation and backbone score maps.
Then, pairwise matching is conducted among candidate proposals by deep learning
based keypoint extraction and matching, i.e., SuperPoint and SuperGlue.
Integrated score map generation and refinement methods are designed to
integrate results of both stages and obtain optimized results. Our two-stage
framework unifies end-to-end deep matching and keypoint matching by obtaining
highly suspected proposals, and opens a new gate for deep learning research in
copy-move forgery detection. Experiments on publicly available datasets
demonstrate the effectiveness of our two-stage framework.
