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An efficient copy move forgery detection using deep learning feature extraction and matching algorithm

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Abstract

The image forgery activities are on the rise because of the development of various image editing tools. Such activities are done by attackers with intentions of defaming people and websites or for gaining monetary advantage, extortion etc. Image forgeries are carried out through various ways, among one is the copy-move forgery. The basic process of copy-move image forgery is copying the objects present in an image and create the new image by using the copied objects or placing the copied object on the same image on a different location, hence the need for a forgery detection system to protect the authenticity of images. The existing forgery detection techniques detect the tampered regions with less efficiency because of the large size and lower contrast of the images. This article proposes an efficient technique for detecting the copy-move forged image based on deep learning. The proposed algorithm initializes the tampered image as the input for our system to detect the tampered region. Our system includes processes like segmentation, feature extraction, dense depth reconstruction, and finally identifying the tampered areas. The proposed deep learning based system can save on computational time and detect the duplicated regions with more accuracy.

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Authors and Affiliations

  1. Delhi Technological University, New Delhi, India

    Ritu Agarwal & Om Prakash Verma

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  1. Ritu Agarwal
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  2. Om Prakash Verma
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Correspondence to Ritu Agarwal.

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Agarwal, R., Verma, O.P. An efficient copy move forgery detection using deep learning feature extraction and matching algorithm. Multimed Tools Appl 79, 7355–7376 (2020). https://doi.org/10.1007/s11042-019-08495-z

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  • DOI: https://doi.org/10.1007/s11042-019-08495-z

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