Enhanced Fiilltterr-based SIFT Apprroach fforr Copy-Move Forrgerry Dettecttiion | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 10, Volume 28, Issue 1, January 2019, Page 159-182 PDF (746.44 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2019.62749 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Mohamed Elaskily* 1; Heba Aslan1; Mohamed Dessouky2; Fathi Abd El-Samie3; Osama Faragallah4; Osama Elshakankiry4 | ||||
| 1Dept. of Informatics, Electronics Research Institute. | ||||
| 2Dept. of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University. | ||||
| 3Dept. of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University. | ||||
| 4Dept. of Information Technology, College of Computers and Information Technology, Taif University | ||||
| Abstract | ||||
| Image forgeries are applied to give the digital images other meanings or to deceive the viewers. Image forgeries appear in many cases such as judges in courts, cybercrimes, military and intelligence deception, or defamation of important characters. There are many different types of image forgeries such as copy move forgery, image retouching, image splicing, image morphing, and image resampling. Copy move forgery is the widest type and easy to apply between all digital image forgeries. Scale Invariant Features Transform (SIFT) algorithm is used strongly to detect copy move forgeries due to its efficiency in digital image analysis. SIFT algorithm is extracting image features, which are invariant to geometrical transformations such as scaling, translation, and rotation. These features are used in performing the matching between different views of a scene or an object. This paper enhances the efficiency of using SIFT algorithm in detecting copy move forgery by two ways. Firstly, it enhances the image itself by applying different types of digital filters to reinforce the image features giving the ability to detect forgeries. Butterworth low-pass filter, a high-pass filter, and the combination of them are applied to this task. Secondly, the matching strategy is adapted based on a new thresholding approach to increase the true positive rate and decrease the false positive rate. Experimental results show that the proposed approach gives better results compared with traditional copy-move detection approaches. In addition, it gives better stability and reliability to different copy-move forgery conditions. | ||||
| References | ||||
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