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Copy-move forgery detection technique based on discrete cosine transform blocks features

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
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Abstract

With the increasing number of software applications that allow altering digital images and their ease of use, they weaken the credibility of an image. This problem, together with the ease of distributing information through the Internet (blogs, social networks, etc.), has led to a tendency for information to be accepted as true without its veracity being questioned. Image counterfeiting has become a major threat to the credibility of the information. To deal with this threat, forensic image analysis is aimed at detecting and locating image forgeries using multiple clues that allows it to determine the veracity or otherwise of an image. In this paper, we present a method for the authentication of images. The proposed method performs detection of copy-move alterations within an image, using the discrete cosine transform. The characteristics obtained from these coefficients allow us to obtain transfer vectors, which are grouped together. Through the use of a tolerance threshold, it is possible to determine whether there are regions copied and pasted within the analysed image. The results obtained from the experiments reported in this paper demonstrate the effectiveness of the proposed method. For the evaluation of the proposed methods, experiments were carried out with public databases of falsified images that are widely used in the literature.

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 700326. Website: http://ramses2020.eu

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Authors and Affiliations

  1. Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040, Madrid, Spain

    Esteban Alejandro Armas Vega, Edgar González Fernández, Ana Lucila Sandoval Orozco & Luis Javier García Villalba

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  1. Esteban Alejandro Armas Vega
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  2. Edgar González Fernández
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  3. Ana Lucila Sandoval Orozco
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  4. Luis Javier García Villalba
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Correspondence to Luis Javier García Villalba.

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Armas Vega, E.A., González Fernández, E., Sandoval Orozco, A.L. et al. Copy-move forgery detection technique based on discrete cosine transform blocks features. Neural Comput & Applic 33, 4713–4727 (2021). https://doi.org/10.1007/s00521-020-05433-1

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