Robust method for detection of copy-move forgery in digital images

Khan, Saiqa; Kulkarni, Arun D.

doi:10.1109/icsip.2010.5697444

Cited by 22 publications

(4 citation statements)

References 2 publications

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“…Image tampering is defined as "adding or removing important features from an image without leaving any obvious traces of tampering" and thus image tampering is considered as intentional manipulation of images for malicious purposes [3]. There are various techniques for counterfeiting images and these can be classified into three broad categories.…”

Section: Image Tamperingmentioning

confidence: 99%

A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching

Gupta

2018

Bulletin EEI

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With the development of Image processing editing tools and software, an image can be easily manipulated. The image manipulation detection is vital for the reason that an image can be used as legal evidence, in the field of forensics investigations, and also in numerous various other fields. The image forgery detection based on pixels aims to validate the digital image authenticity with no aforementioned information of the main image. There are several means intended for tampering a digital image, for example, copy-move or splicing, resampling a digital image (stretch, rotate, resize), removal as well as the addition of an object from your image. Copy move image forgery detection is utilized to figure out the replicated regions as well as the pasted parts, however forgery detection may possibly vary dependant on whether or not there is virtually any post-processing on the replicated part before inserting the item completely to another party. Typically, forgers utilize many operations like rotation, filtering, JPEG compression, resizing as well as the addition of noise to the main image before pasting, that make this thing challenging to recognize the copy move image forgery. Hence, forgery detector needs to be robust to any or all manipulations and also the latest editing software tools. This research paper illustrates recent issues in the techniques of forgery detection and proposes a advanced copy–move forgery detection scheme using adaptive over-segmentation and feature point matching. The proposed scheme integrates both block-based and key point-based forgery detection methods.

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Section: Image Tamperingmentioning

confidence: 99%

A New Copy Move Forgery Detection Technique using Adaptive Over-segementation and Feature Point Matching

Gupta

2018

Bulletin EEI

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“…The blind forensic technique for the copy/move forgery detection reported in [30, 31] uses DWT to reduce dimensions and to generate the overlapping blocks of the compressed image. Thereafter, the blocks are sorted lexicographically and phase correlation is used for similar block checking.…”

Section: Detection Of Copy/move Forgerymentioning

confidence: 99%

Survey on blind image forgery detection

Qazi

Hayat

Khan

et al. 2013

IET Image Processing

102

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With the mushroom growth of state-of-the-art digital image and video manipulations tools, establishing the authenticity of multimedia content has become a challenging issue. Digital image forensics is an increasingly growing research field that symbolises a never ending struggle against forgery and tampering. This survey attempts to cover the blind techniques that have been proposed for exposing forgeries. This work dwells on the detection techniques for three of the most common forgery types, namely copy/move, splicing and retouching.

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“…Copy move forgery detection methods using passive approach for detection can be divided into two groups: Block based methods [3], [4], [5], [10] and keypoint based methods [6], [7], [8].The very first passive method for detection of copy move forgery is presented by Fridrich et al [3] that uses Discrete Cosine Transform (DCT) for the detection of copy move forgery. In this method the image is divided into overlapping blocks and then DCT is performed on each block.…”

Section: Literature Surveymentioning

confidence: 99%

“…This method handles minor variations in rotation and scaling. In [4] the authors use the Discrete Wavelet Transform to compress the image, then the compressed image is divided into overlapping blocks and phase correlation is used as similarity criterion to identify the duplicate blocks in an image. This method shows robustness towards noise and jpeg compression but fails to be rotation and scale invariant.…”

Section: Literature Surveymentioning

confidence: 99%