Image Copy-Move Forgery Detection via Deep Cross-Scale PatchMatch

TL;DR

This paper introduces a novel deep cross-scale patchmatch framework for image copy-move forgery detection that improves generalizability and localizes manipulated regions more reliably than existing methods.

Contribution

It presents a new end-to-end differentiable CMFD model combining deep and conventional techniques with a cross-scale patchmatch for precise region localization.

Findings

Achieves higher accuracy than existing methods.

Demonstrates strong generalizability across different forgery scenarios.

Outperforms current approaches in experimental evaluations.

Abstract

The recently developed deep algorithms achieve promising progress in the field of image copy-move forgery detection (CMFD). However, they have limited generalizability in some practical scenarios, where the copy-move objects may not appear in the training images or cloned regions are from the background. To address the above issues, in this work, we propose a novel end-to-end CMFD framework by integrating merits from both conventional and deep methods. Specifically, we design a deep cross-scale patchmatch method tailored for CMFD to localize copy-move regions. In contrast to existing deep models, our scheme aims to seek explicit and reliable point-to-point matching between source and target regions using features extracted from high-resolution scales. Further, we develop a manipulation region location branch for source/target separation. The proposed CMFD framework is completely differentiable and can be trained in an end-to-end manner. Extensive experimental results demonstrate the high generalizability of our method to different copy-move contents, and the proposed scheme achieves significantly better performance than existing approaches.