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An efficient approach for copy-move image forgery detection using convolution neural network

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Abstract

Digital imaging has become elementary in this novel era of technology with unconventional image forging techniques and tools. Since, we understand that digital image forgery is possible, it cannot be even presented as a piece of evidence anywhere. Dissecting this fact, we must dig unfathomable into the issue to help alleviate such derelictions. Copy-move and splicing of images to create a forged one prevail in this monarchy of digitalization. Copy-move involves copying one part of the image and pasting it to another part of the image while the latter involves merging of two images to significantly change the original image and create a new forged one. In this article, a novel slant using a convolutional neural network (CNN) has been proposed for automatic detection of copy-move forgery detection. For the experimental work, a benchmark dataset namely, MICC-F2000 is considered which consists of 2000 images in which 1300 are original and 700 are forged. The experimental results depict that the proposed model outperforms the other traditional methods for copy-move forgery detection. The results of copy-move forgery were highly promising with an accuracy of 97.52% which is 2.52% higher than the existing methods.

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Authors and Affiliations

  1. Department of Computer Science & Technology, Central University of Punjab, Bathinda, India

    Saboor Koul, Surinder Singh Khurana & Faisel Mushtaq

  2. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, India

    Munish Kumar

  3. Department of Information Technology, University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Krishan Kumar

Authors
  1. Saboor Koul
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  2. Munish Kumar
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  3. Surinder Singh Khurana
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  4. Faisel Mushtaq
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  5. Krishan Kumar
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Correspondence to Munish Kumar.

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Koul, S., Kumar, M., Khurana, S.S. et al. An efficient approach for copy-move image forgery detection using convolution neural network. Multimed Tools Appl 81, 11259–11277 (2022). https://doi.org/10.1007/s11042-022-11974-5

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  • DOI: https://doi.org/10.1007/s11042-022-11974-5

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