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A novel deep learning framework for copy-moveforgery detection in images

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Abstract

In this era of technology, digital images turn out to be ubiquitous in a contemporary society and they can be generated and manipulated by a wide variety of hardware and software technologies. Copy-move forgery is considered as an image tampering technique that aims to generate manipulated tampered images by concealing unwanted objects or reproducing desirable objects within the same image. Therefore, image content authentication has become an essential demand. In this paper, an innovative design for automatic detection of copy-move forgery based on deep learning approaches is proposed. A Convolutional Neural Network (CNN) is specifically designed for Copy-Move Forgery Detection (CMFD). The CNN is exploited to learn hierarchical feature representations from input images, which are used for detecting the tampered and original images. The extensive experiments demonstrate that the proposed deep CMFD algorithm outperforms the traditional CMFD systems by a considerable margin on the three publicly accessible datasets: MICC-F220, MICC-F2000, and MICC-F600. Furthermore, the three datasets are incorporated and joined to the SATs-130 dataset to form new combinations of datasets. An accuracy of 100% has been achieved for the four datasets. This proves the robustness of the proposed algorithm against a diversity of known attacks. For better evaluation, comparative results are included.

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Author information

Authors and Affiliations

  1. Informatics Department, Electronics Research Institute (ERI), Cairo, Egypt

    Mohamed A. Elaskily & Heba K. Aslan

  2. Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Mohamed A. Elaskily, Mohamed M. Dessouky, Osama A. Elshakankiry & Osama S. Faragallah

  3. Computers and Systems Department, Electronics Research Institute (ERI), Cairo, Egypt

    Heba A. Elnemr

  4. Department of the Robotics and Inteligent Machines, Faculty of Artificial Inteligence, Kafrelsheikh University, Kafrelsheikh, Egypt

    Ahmed Sedik

  5. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ghada M. El Banby

  6. Department of Information Technology, College of Computers and Information Technology, Taif University, Taif University, Al-Hawiyah, 21974, Saudi Arabia

    Osama A. Elshakankiry & Osama S. Faragallah

  7. Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61111, Egypt

    Ashraf A. M. Khalaf

  8. Department Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Fathi E. Abd El-Samie

  9. Department of Information Technology, College of Computer and Information sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

  10. Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Saudi Arabia

    Mohamed M. Dessouky

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  1. Mohamed A. Elaskily
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  2. Heba A. Elnemr
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  3. Ahmed Sedik
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  10. Fathi E. Abd El-Samie
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Correspondence to Mohamed A. Elaskily.

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Elaskily, M.A., Elnemr, H.A., Sedik, A. et al. A novel deep learning framework for copy-moveforgery detection in images. Multimed Tools Appl 79, 19167–19192 (2020). https://doi.org/10.1007/s11042-020-08751-7

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