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State of the art in passive digital image forgery detection: copy-move image forgery

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Abstract

Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is copy-move digital image forgery, which involves copying one part of the image onto another area of the same image. Various methods have been proposed to detect copy-move forgery that uses different types of transformations. The goal of this paper is to determine which copy-move forgery detection methods are best for different image attributes such as JPEG compression, scaling, rotation. The advantages and drawbacks of each method are also highlighted. Thus, the current state-of-the-art image forgery detection techniques are discussed along with their advantages and drawbacks.

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Funding was provided by Universiti Malaya (UM) (Grant No. RG312-14AFR).

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Somayeh Sadeghi, Hamid A. Jalab & Diaa Uliyan

  2. Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan

    Sajjad Dadkhah

  3. Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica Universit degli Studi di Palermo, viale delle Scienze bd.6, Palermo, Italy

    Giuseppe Mazzola

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  1. Somayeh Sadeghi
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  2. Sajjad Dadkhah
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Sadeghi, S., Dadkhah, S., Jalab, H.A. et al. State of the art in passive digital image forgery detection: copy-move image forgery. Pattern Anal Applic 21, 291–306 (2018). https://doi.org/10.1007/s10044-017-0678-8

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