Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal SuperGlue

• URL: http://arxiv.org/abs/2012.08697v1
• Date: Wed, 16 Dec 2020 02:05:55 GMT
• Title: Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal SuperGlue
• Authors: Yaqi Liu and Chao Xia and Xiaobin Zhu and Shengwei Xu
• Abstract summary: Copy-move forgery detection identifies a tampered image by detecting pasted and source regions in the same image. We propose a novel two-stage framework specially for copy-move forgery detection. Proposal SuperGlue is proposed to remove false-alarmed regions and remedy incomplete regions.
• Score: 9.676233763589618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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