Copy-Move Forgery Detection Based on Discrete and SURF Transforms

  • Faten Maher Al_azrak
  • Zeinab F. Elsharkawy
  • Ahmed S. Elkorany
  • Ghada M. El Banby
  • Moawad I. Dessowky
  • Fathi E. Abd El-SamieEmail author


As a result of the rapid progress in editing techniques, fakes and forgeries in images became easy and pervasive. Image forgery detection methods have been implemented to reveal the image rig. Copy-move forgery is a type of forgery in which a part of the image is copied to another location of the same image to hide important information or duplicate certain objects in the original image, which makes the viewer suffer from difficulties to detect the tampered region. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features on the copied parts are similar to those of other parts of the image. This paper presents two approaches for forgery detection: one based on discrete transforms and the other based on Speeded-UP Robust Feature (SURF) transform. In the first framework, a comparison is presented between different trigonometric transforms in 1D and 2D for the objective of forgery detection. This comparison study is based on the completeness rate and the time of processing for the detection. This comparison gives a conclusion that the DFT in 1D or 2D implementation is the best choice to detect copy-move forgery compared to other trigonometric transforms. For the SURF-based framework, the image is divided into blocks with 50% overlapping. SURF features are extracted for each block and the complementary image to this block. A matching process is performed on the SURF keypoints of the block and the complementary image. The number of matched keypoints between each block of interest and its complementary image is recorded. The whole image is treated on a block-by-block basis yielding 49 matching scores in a distinctive feature vector. The correlation matrix for this feature vector is created and decomposed with Singular Value Decomposition (SVD) to give singular values used to classify the whole image as being tampered or not. Different types of classifiers have been used and compared. Accuracy levels up to 100% have been recorded.


Image forgery detection Trigonometric transforms Copy-move forgery SURF SVD 



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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Faten Maher Al_azrak
    • 1
  • Zeinab F. Elsharkawy
    • 2
  • Ahmed S. Elkorany
    • 1
  • Ghada M. El Banby
    • 3
  • Moawad I. Dessowky
    • 1
  • Fathi E. Abd El-Samie
    • 1
    Email author
  1. 1.Department of Electronics and Electrical Communications, Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt
  2. 2.Engineering Department, Nuclear Research CenterAtomic Energy AuthorityCairoEgypt
  3. 3.Department of Industrial Electronics and Control Engineering, Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt

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