Image Copy-Move Forgery Detection via Deep PatchMatch and Pairwise Ranking Learning

TL;DR

This paper introduces an end-to-end deep learning framework combining PatchMatch and pairwise ranking to improve the detection of copy-move forgeries in images, especially in challenging scenarios with background blending.

Contribution

It develops a novel deep cross-scale PatchMatch method and a pairwise rank learning framework, enhancing generalizability and accuracy over existing methods.

Findings

Outperforms existing methods in various scenarios

Demonstrates strong generalizability across different datasets

Effectively detects subtle copy-move forgeries

Abstract

Recent advances in deep learning algorithms have shown impressive progress in image copy-move forgery detection (CMFD). However, these algorithms lack generalizability in practical scenarios where the copied regions are not present in the training images, or the cloned regions are part of the background. Additionally, these algorithms utilize convolution operations to distinguish source and target regions, leading to unsatisfactory results when the target regions blend well with the background. To address these limitations, this study proposes a novel end-to-end CMFD framework that integrates the strengths of conventional and deep learning methods. Specifically, the study develops a deep cross-scale PatchMatch (PM) method that is customized for CMFD to locate copy-move regions. Unlike existing deep models, our approach utilizes features extracted from high-resolution scales to seek explicit and reliable point-to-point matching between source and target regions. Furthermore, we propose a novel pairwise rank learning framework to separate source and target regions. By leveraging the strong prior of point-to-point matches, the framework can identify subtle differences and effectively discriminate between source and target regions, even when the target regions blend well with the background. Our framework is fully differentiable and can be trained end-to-end. Comprehensive experimental results highlight the remarkable generalizability of our scheme across various copy-move scenarios, significantly outperforming existing methods.