An Evaluation of Popular Copy-Move Forgery Detection Approaches

TL;DR

This paper systematically evaluates 15 prominent copy-move forgery detection algorithms across various postprocessing scenarios, highlighting the robustness of keypoint-based and block-based features like SIFT, SURF, DCT, DWT, KPCA, PCA, and Zernike.

Contribution

It provides a comprehensive comparison of existing algorithms within a unified framework and introduces a challenging real-world dataset for evaluation.

Findings

SIFT and SURF keypoint features perform best against noise and downsampling.

Block-based DCT, DWT, KPCA, PCA, and Zernike features show high robustness.

The evaluation framework enables systematic comparison of forgery detection methods.

Abstract

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.