# Robust Detection and Localization of Image Copy-Move Forgery Using Multi-Feature Fusion

## TL;DR

This paper introduces a new method for detecting and locating copy-move image forgeries using multi-feature fusion, improving accuracy and robustness against common image manipulations.

## Contribution

A novel multi-feature fusion network and lightweight decoder that enhance feature representation and localization accuracy in copy-move forgery detection.

## Key findings

- The proposed MFFNet model outperforms existing methods in forgery detection under JPEG compression, noise, and resizing.
- The integration of RGB and noise domain features improves the richness of feature representation.
- The LMPD decoder achieves more accurate localization by combining local and global attention mechanisms.

## Abstract

Copy-move forgery detection (CMFD) is a crucial image forensics analysis technique. The rapid development of deep learning algorithms has led to impressive advancements in CMFD. However, existing models suffer from two key limitations: Their feature fusion modules insufficiently exploit the complementary nature of features from the RGB domain and noise domain, resulting in suboptimal feature representations. During decoding, they simply classify pixels as authentic or forged, without aggregating cross-layer information or integrating local and global attention mechanisms, leading to unsatisfactory detection precision. To overcome these limitations, a robust detection and localization approach to image copy-move forgery using multi-feature fusion is proposed. Firstly, a Multi-Feature Fusion Network (MFFNet) was designed. Within its feature fusion module, features from both the RGB domain and noise domain were fused to enable mutual complementarity between distinct characteristics, yielding richer feature information. Then, a Lightweight Multi-layer Perceptron Decoder (LMPD) was developed for image reconstruction and forgery localization map generation. Finally, by aggregating information from different layers and combining local and global attention mechanisms, more accurate prediction masks were obtained. The experimental results demonstrate that the proposed MFFNet model exhibits enhanced robustness and superior detection and localization performance compared to existing methods when faced with JPEG compression, noise addition, and resizing operations.