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Evaluation of copy-move forgery detection: datasets and evaluation metrics

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Abstract

Creating copy-move forgery became even easier using a wide range of software and platforms. Many algorithms have been proposed to solve the problem, but each one of those algorithms has its own drawbacks. Researchers face many challenges in developing copy-move detection algorithms, and in this paper, we focus on two challenges. The first is the benchmark dataset, and the second involves evaluation metrics. In this paper, we investigate the available copy-move datasets and their advantages and disadvantages. In addition, we discuss the different metrics that have been used by researchers to evaluate the copy-move forgery detection (CMFD) algorithms. On that basis, we suggest the standard specifications of the appropriate copy-move dataset and the metrics that should be used to evaluate the detection algorithms. The findings of this paper will help researchers evaluate their algorithms effectively and fairly essential for developing reliable algorithms.

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Notes

  1. Dataset here refers to the pixels of an image.

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Acknowledgments

The authors would like to acknowledge the financial assistance provided by the Ministry of Education Malaysia through FRGS grant number 203/PELECT/6071305.

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  1. School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Penang, Malaysia

    Osamah M. Al-Qershi & Bee Ee Khoo

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  1. Osamah M. Al-Qershi
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Correspondence to Bee Ee Khoo.

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Al-Qershi, O.M., Khoo, B.E. Evaluation of copy-move forgery detection: datasets and evaluation metrics. Multimed Tools Appl 77, 31807–31833 (2018). https://doi.org/10.1007/s11042-018-6201-4

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