7 research outputs found

    Coarse-to-fine copy-move image forgery detection method based on discrete cosine transform

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    Digital image forgery has become a serious problem in the present society. As the world is advancing in the information and communication technology, it has become more crucial for researchers to take part in overcoming the wide-spreading digital image forgery to prove an image authenticity especially when the legislative field is involved. Copy-move forgery is a type of image forgery where one part of an image is copied and pasted in other regions of the same image, and it is one of the most common image forgeries to conceal some information in the original image. There are numerous techniques available to detect copy-move forgeries which each of them have their own advantages and drawbacks. Discrete Cosine Transform (DCT) is a powerful algorithm developed as a method to detect copy-move forgery which is well known for its detection efficiency. However, the detection rate relies intensely on the size of block used. Small block size is known for its ability to detect fine cloned objects, but the drawback is it produces too many false positive and requires high execution time. In this research, a method to overcome the weaknesses of using small block size by applying the coarse-to-fine approach with the two-tier process is proposed. The proposed method is evaluated on fifteen forged images on the CoMoFoD dataset. The results demonstrated that the proposed method is able to achieve high precision and recall rate of over 90% as well as improves the computation time by reducing the overall duration of forgery detection up to 73% compared to the traditional DCT method using small block size. Therefore, these findings validate that the proposed method offers a trade-off between accuracy and runtime

    Review on local binary patterns variants as texture descriptors for copy-move forgery detection

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    Past decades had seen the concerned by researchers in authenticating the originality of an image as the result of advancement in computer technology. Many methods have been developed to detect image forgeries such as copy-move, splicing, resampling and et cetera. The most common type of image forgery is copy-move where the copied region is pasted on the same image. The existence of high similarity in colour and textures of both copied and pasted images caused the detection of the tampered region to be very difficult. Additionally, the existence of post-processing methods makes it more challenging. In this paper, Local Binary Pattern (LBP) variants as texture descriptors for copy-move forgery detection have been reviewed. These methods are discussed in terms of introduction and methodology in copy-move forgery detection. These methods are also compared in the discussion section. Finally, their strengths and weaknesses are summarised, and some future research directions were pointed out

    Detection of intentionally made changes in image content

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    Digital images and video signals represent the most frequently transmitted contents. Namely, with the development of modern digital cameras and smartphones, the use of multimedia content increases every day. They are used in everyday life, for getting information and also as authenticated proofs or corroboratory evidence in different areas like: forensic studies, law enforcement, journalism and others...Multifraktalna analiza se pokazala kao dobar alat za analizu postojećih slika, kao i segmentaciju određenih regiona, izdvajanje ivica, uglova slike i slično. Kako kopirani i nalepljeni delovi imaju sličnu strukturu, može se primeniti multifraktalna analiza, koja u osnovi analizira samosličnost. Multifraktalni spektar daje globalni opis slike (ili, opštije, fenomena koji se ispituje). Vrednost Hölder-ovog eksponenta zavisi od položaja u strukturi i opisuje lokalnu regularnost signala. Naime, različiti objekti na slici imaju različite spektre, različite pozicije maksimuma, minimuma, prve nule itd, što se pokazalo kao interesantan skup različitih parametara pomoću kojih se mogu detektovati namerne promene na slikama..

    UMSL Bulletin 2020-2021

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    The 2020-2021 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1084/thumbnail.jp

    UMSL Bulletin 2019-2020

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    The University Bulletin/Course Catalog 2019-2020 Edition.https://irl.umsl.edu/bulletin/1083/thumbnail.jp

    UMSL Bulletin 2021-2022

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    The 2021-2022 Bulletin and Course Catalog for the University of Missouri St. Louis. This is the July 1, 2021 pdf snapshot version of the University Bulletin and Course Catalog.https://irl.umsl.edu/bulletin/1086/thumbnail.jp
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