An Authentication Method for Copy Areas of Images

Wang, Zhenguo; Geng, Zexun

doi:10.1109/icig.2007.120

Cited by 10 publications

(7 citation statements)

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“…CMFD methods are either keypointbased methods (e. g. [3], [11], [22]) or block-based methods (e. g. [4]- [7], [9], [10], [12]- [21], [23]- [28]). In both cases, preprocessing of the images is possible.…”

Section: Typical Workflow For Copy-move Forgery Detectionmentioning

confidence: 99%

“…a) Matching: High similarity between two feature descriptors is interpreted as a cue for a duplicated region. For block-based methods, most authors propose the use of lexicographic sorting in identifying similar feature vectors (see e. g. [4]- [7], [9], [10], [12]- [19], [21], [23]- [28]). In lexicographic sorting a matrix of feature vectors is built so that every feature vector becomes a row in the matrix.…”

Section: Typical Workflow For Copy-move Forgery Detectionmentioning

confidence: 99%

See 1 more Smart Citation

An Evaluation of Popular Copy-Move Forgery Detection Approaches

Christlein¹,

Rieß²,

Jordan³

et al. 2012

IEEE Trans.Inform.Forensic Secur.

697

486

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Abstract-A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e. g. , matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and ZERNIKE features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.

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Section: Typical Workflow For Copy-move Forgery Detectionmentioning

confidence: 99%

Section: Typical Workflow For Copy-move Forgery Detectionmentioning

confidence: 99%

An Evaluation of Popular Copy-Move Forgery Detection Approaches

Christlein¹,

Rieß²,

Jordan³

et al. 2012

IEEE Trans.Inform.Forensic Secur.

697

486

View full text Add to dashboard Cite

show abstract

“…Popescu and Farid [14] used the PCA domain representation to detect the forged part, even when the copied area is corrupted by noise. Ju et al [15] adopted PCA for small image blocks with fixed sizes, and generates the degraded dimensions representation for copy-cover detection.…”

Section: Copy-cover Image Forgery Detectionmentioning

confidence: 99%

A Comparison Study on Copy-Cover Image Forgery Detection~!2010-01-09~!2010-04-05~!2010-05-14~!

Shih¹,

Yuan²

2010

TOAIJ

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Due to rapid advances and availabilities of powerful image processing software, digital images are easy to manipulate and modify for ordinary people. This makes it more and more difficult for a viewer to check the authenticity of a given digital image. For digital photographs to be used as evidence in law issues or to be circulated in mass media, it is inevitably needed to identify whether an image is authentic or not. In this paper, we discuss the techniques of copy-cover image forgery and compare four detection methods for copy-cover forgery detection, which are based on PCA, DCT, spatial domain, and statistical domain. We investigate their effectiveness and sensitivity under the influences of Gaussian blurring and lossy JPEG compressions. It is concluded that the PCA method outperforms the others in terms of time complexity and accuracy. In JPEG compression simulation, its true positive rate is above 90% and false positive rate is above 99%. In Gaussian blurring simulation, its true positive rate is above 77% and false positive rate is above 99%.

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“…Block-based methods [8][9][10][11][12][13][14][15][16][17][18][19] are suitable to deal with those smooth images. However, the time complexity is high since the image is divided into a lot of overlapping blocks.…”

Section: Introductionmentioning

confidence: 99%