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

TL;DR

This paper introduces a two-stage deep learning framework for copy-move forgery detection, combining self deep matching with Proposal SuperGlue to improve accuracy and reduce false positives.

Contribution

It presents a novel two-stage approach integrating hierarchical feature extraction, spatial attention, and deep keypoint matching for enhanced copy-move forgery detection.

Findings

Effective detection on public datasets

Reduces false alarms and improves region completeness

Unifies end-to-end deep matching with keypoint matching

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.