Improved DSIFT descriptor based copy-rotate-move forgery detection

Khayeat, Ali, Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 2016. Improved DSIFT descriptor based copy-rotate-move forgery detection. Presented at: PSIVT 2015: 7th Pacific Rim Symposium on Image and Video Technology, Auckland, New Zealand, 25-27November 2015. Image and Video Technology. Lecture Notes in Computer Science , vol.9431 (9431) Springer, pp. 642-655. 10.1007/978-3-319-29451-3_51

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Abstract

In recent years, there has been a dramatic increase in the number of images captured by users. This is due to the wide availability of digital cameras and mobile phones which are able to capture and transmit images. Simultaneously, image-editing applications have become more usable, and a casual user can easily improve the quality of an image or change its content. The most common type of image modification is cloning, or copy-move forgery (CMF), which is easy to implement and difficult to detect. In most cases, it is hard to detect CMF with the naked eye and many possible manipulations (attacks) can be used to make the doctored image more realistic. In CMF, the forger copies part(s) of the image and pastes them back into the same image. One possible transformation is rotation, where an object is copied, rotated and pasted. Rotation-invariant features need to be used to detect Copy-Rotate-Move (CRM) forgery. In this paper we present three contributions. First, a new technique to detect CMF is developed, using Dense Scale-Invariant Feature Transform (DSIFT). Second, a new improved DSIFT descriptor is implemented which is more robust to rotation than Zernike moments. Third, a new method to remove false matching is proposed. Extensive experiments have been conducted to train, evaluate and test the algorithms, the new feature vector and the suggested method to remove false matching. We show that the proposed method can detect forgery in images with blurring, brightness change, colour eduction, JPEG compression, variations in contrast and added noise.

Item Type:	Conference or Workshop Item (Poster)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
ISBN:	9783319294506
ISSN:	0302-9743
Date of First Compliant Deposit:	30 March 2016
Last Modified:	18 Nov 2022 03:03
URI:	https://orca.cardiff.ac.uk/id/eprint/86551

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