Detection of Copy-Rotate-Move Forgery Using Zernike Moments

Ryu, Sang Young; Lee, Min‐Jeong; Lee, Heung-Kyu

doi:10.1007/978-3-642-16435-4_5

Cited by 223 publications

(121 citation statements)

References 7 publications

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“…One of the efficient block based methods which gave the most precise results [4] was presented in [8] during 2010. The work was aimed at detecting copy-rotate-move forgery using Zernike moments which have desirable properties like rotation invariance, robustness to noise etc.…”

Section: Block-based Methodsmentioning

confidence: 99%

“…It can detect arbitrary variations in rotation and scaling in the copied part and is a processing scheme in which any suitable feature can be used in combination with the SATS post processing. It was tested in combination with the previous method of Ryu [8] and nearly 75% of the duplicated blocks with rotation were detected and the method was found to be accurate for JPEG compression with Q factor 50 to 100.…”

Section: Fig 2: Common Processing Pipeline For Copy-move Forgery Detementioning

confidence: 99%

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Recent Block-based Methods of Copy-Move Forgery Detection in Digital Images

Sekhar¹,

S²

2014

IJCA

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Photographs are taken as valid evidences in various scenarios of our day to day life. Because of the developments in the field of Image Processing, altering images according to ones need is not a difficult task. Techniques of Image Forensics play its crucial role at this juncture. One of the mostly found types of image tampering is Copy-Move forgery. A copymove forgery is performed by copying a region in an image and pasting it on another region in the same image, mostly after some form of post-processing like rotation, scaling, blurring, noise addition, JPEG compression etc. Two types of copy-move forgery detection techniques exist in literature. They are the Block based methods and Key-point based methods. Both the methods have their own advantages and limitations. This paper presents a survey on the recent developments in block based methods.

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Section: Block-based Methodsmentioning

confidence: 99%

Section: Fig 2: Common Processing Pipeline For Copy-move Forgery Detementioning

confidence: 99%

Recent Block-based Methods of Copy-Move Forgery Detection in Digital Images

Sekhar¹,

S²

2014

IJCA

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“…Ryu et al [9] divided the image into overlapping 24×24 blocks and calculated the Zernike moments for each block. They sorted the Zernike feature vectors lexicographically and computed the Euclidean distance between adjacent stored blocks.…”

Section: Related Workmentioning

confidence: 99%

Improved DSIFT Descriptor Based Copy-Rotate-Move Forgery Detection

Khayeat

Sun

Rosin

2016

Image and Video Technology

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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 modication is cloning, or copy-move forgery (CMF), which is easy to implement and dicult 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 presented 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 reduction, JPEG compression, variations in contrast and added noise.

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“…Good forgery detection method should be robust to some types of transformations, such as scaling, rotations and JPEG compression and Gaussian Noise addition. Recently, an attempt was made in [12] to identify copy-move forgery using Zernike moments. Their method detects duplicated region rotated some angle before it is pasted.…”

Section: Fig 1 the Photo (Right) Is A Tampered With Original (Left)mentioning

confidence: 99%

Automated Forensic Method for CopyMove Forgery Detection based on Harris Interest Points and SIFT Descriptors

Shivakumar¹,

Santhosh²

2011

IJCA

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We are undoubtedly living in an age where we are exposed to a remarkable array of visual imagery. Nowadays, accepting digital images of official documents is common practice. Image authenticity is important in many social areas. For instance, the trustworthiness of photographs has an essential role in courtrooms, where they are used as evidence. In the medical field, physicians make critical decisions based on digital images. The technology today makes it convenient to quickly exchange contracts, photographs or other documents. While we may have historically had confidence in the integrity of this imagery, today's digital technology has begun to erode this trust. With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. It is possible to change the information represented by an image and create forgeries, which are indistinguishable by naked eye from authentic photographs and documents. In the proposed method Harris Interest Point detector along with SIFT descriptors are used to detect copymove forgery. KD-Tree is used for matching.

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Detection of Copy-Rotate-Move Forgery Using Zernike Moments

Cited by 223 publications

References 7 publications

Recent Block-based Methods of Copy-Move Forgery Detection in Digital Images

Recent Block-based Methods of Copy-Move Forgery Detection in Digital Images

Improved DSIFT Descriptor Based Copy-Rotate-Move Forgery Detection

Automated Forensic Method for CopyMove Forgery Detection based on Harris Interest Points and SIFT Descriptors

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