Abstract
Digital image forgery is a nightmare in the current scenario. The authenticity of images that circulates through media and public outlets is therefore critical with a caution: “Do not believe everything you see”. Copy-move forgery is the most frequently created image forgery that conceals a particular feature from the scene by replacing it with another feature of the same image. In this paper, a hybrid approach based on local fractal dimension (LFD) and singular value decomposition (SVD) to efficiently detect and localize the copy-move forged region is proposed. In order to reduce the computational complexity of the classification procedure, we propose to arrange image blocks in a B+ tree structure ordered based on the LFD values. Pair of blocks within each segment is compared using singular values, to find regions that exhibit maximum resemblance. This reduces the need for comparison to the most suspected image portions alone. The experimental results show how effectively the method identifies the duplicated region; also presents the robustness of the method to detect and localize forgery even in the presence of after-copying manipulations such as rotation, blurring and noise addition. Furthermore it also detect multiple copy-move forgery within the image.
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Oommen, R.S., Jayamohan, M., Sruthy, S. (2016). Scale Invariant Detection of Copy-Move Forgery Using Fractal Dimension and Singular Values. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_47
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DOI: https://doi.org/10.1007/978-3-319-28658-7_47
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