Region duplication forgery detection technique based on keypoint matching / Diaa Mohammed Hassan Uliyan

Diaa , Mohammed Hassan Uliyan (2016) Region duplication forgery detection technique based on keypoint matching / Diaa Mohammed Hassan Uliyan. PhD thesis, University of Malaya.

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    Manipulation of digital images is not considered a new thing nowadays. For as long as cameras have existed, photographers have been staged and images have been forged and passed off for more nefarious purposes. Region duplication is regarded as an efficient and simple operation for image forgeries, where a part of the image itself is copied and pasted into a different part of the same image grid. The detection of duplicated regions can be a challenging task in digital image forensic (DIF) when images are used as evidence to influence the judgment, such as in court of law. Existing methods have been developed in the literature to reveal duplicated regions. These methods are classified into block-based and key point-based methods. Most prior block based methods rely on exhaustive block matching on image contents and suffer from their inability to localize this type of forgery when the duplicated regions have gone through some geometric transformation operations and post-processing operations. In this research, we propose three novel approaches for detecting duplicate regions in forged images that are robust to common geometric transformations and post processing operations. In the first approach, we propose a novel method for detecting uniform and non-uniform duplicated regions with small size in forged images that is robust to geometric transformation operations such as rotation and scaling. The proposed method have adopted statistical region merging (SRM) algorithm to detect small regions, and then Harris interest points are localized in angular radial partition (ARP) of a circular region which are invariant to rotation and scale transformations. Moreover, feature vectors for a circular patch around Harris points are extracted using Hӧlder estimation regularity based descriptor (HGP-2) to reduce false positives. In the second approach, we therefore proposed a forensic algorithm to recognize the blurred duplicate regions in a synthesized forged image efficiently, especially when the forged region in the images is small. The method is based on blur metric evaluation (BME) and phase congruency (PCy). In the third approach, we proposed a detection method to reveal the forgery under illumination variations. The proposed method used Hessian to detect the keypoints and their corresponding features are represented by robust descriptor known as Center symmetric local binary pattern (CSLBP). The proposed methods be evaluated on two benchmark datasets. The first one is MICC-F220 which contains 220 JPEG images. The second dataset is an image manipulation dataset which includes 48 PNG true color. The experimental results illustrate that the proposed algorithms are robust against several geometric changes, such as JPEG compression, rotation, noise, blurring, illumination variations, and scaling. Furthermore, the proposed methods are resistant to forgery where small up to 8*8 pixels and flat regions are involved, with little visual structures. The average detection rate of our algorithm maintained 96 % true positive rate and 7 % false positive rate which outperform several current detection methods.

    Item Type: Thesis (PhD)
    Additional Information: Thesis (PhD) – Faculty of Computer Science & Information Technology, University of Malaya, 2016.
    Uncontrolled Keywords: Digital image forensic (DIF); Center symmetric local binary pattern (CSLBP); Hӧlder estimation regularity based descriptor (HGP-2); Blur metric evaluation (BME); Image forgeries
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Q Science > QA Mathematics > QA76 Computer software
    Divisions: Faculty of Computer Science & Information Technology
    Depositing User: Mr Mohd Safri Tahir
    Date Deposited: 07 Apr 2021 02:15
    Last Modified: 07 Apr 2021 02:15

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