Tamper Detection Based on Regularity of Wavelet Transform Coefficients

Sutcu, Yagiz; Coskun, Baris; Sencar, Hüsrev Taha; Memon, Nasir

doi:10.1109/icip.2007.4378975

Cited by 43 publications

(19 citation statements)

References 18 publications

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“…The method uses artifacts introduced into an image's histogram during the enhancement operations. Yagiz Sutcu et al [127] proposed a forgery detection method based on regularity properties of wavelet coefficients used for estimating sharpness and blurriness of edges. Xin Wang et al [137] proposed an image forgery detection based on the consistency of defocus blur.…”

Section: Blur and Sharpeningmentioning

confidence: 99%

A bibliography on blind methods for identifying image forgery

Mahdian

Saic

2010

Signal Processing: Image Communication

171

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Section: Blur and Sharpeningmentioning

confidence: 99%

A bibliography on blind methods for identifying image forgery

Mahdian

Saic

2010

Signal Processing: Image Communication

171

View full text Add to dashboard Cite

“…These limitations include dependency to image format, sensi tivity to spliced region size and spatial alignment, sensitivity to lightning, dependency to the size of objects and dependency to camera type. (2) Our proposed approach has the ability to distinguish a wide range of blur degrees applicable for image splicing detection. (3) Our proposed approach can detect image splicing in high blur degree images with higher performance than the previous approaches.…”

Section: Introductionmentioning

confidence: 99%

Splicing detection in out-of-focus blurred images

Bahrami

Kot

Fan

2013

2013 IEEE International Workshop on Information Forensics and Security (WIFS)

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This paper proposes a novel framework for image splicing detection based on the inconsistency in the blur degree and depth information of an image. Firstly, the blur kernels of image blocks (local blur kernels) are estimated. Next, a multi step reblurring technique is used to measure the relative blur degrees of the local blur kernels. The relative blur degrees are used as a feature to classify the image blocks based on the blur degrees. Furthermore, block-based and pixel-based techniques are incorporated for a fine segmentation of the regions with different blur degrees. Finally, the consistency in the blur degree and depth information of the regions are analyzed. Any inconsistency in the blur degree and depth information can be used as an evidence of image splicing. The experimental results show that the proposed approach can discriminate a wide range of blur degrees in natural and artificial blurred images which outperforms the state-of-the-art approaches. Furthermore, our proposed approach has been evaluated for image splicing detection. The result shows that it can be used successfully for detection of image splicing.

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“…Some works [10][11][12][13][14] have been proposed for image tampering detection based on blur degree inconsistency. However, these methods can not detect blur type inconsistency.…”

Section: Introductionmentioning

confidence: 99%

Image tampering detection by exposing blur type inconsistency

Bahrami

Kot

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a novel method for image tampering detection in multi-type blurred images. After block-based image partitioning, a space-variant prior for local blur kernels is proposed for local blur kernels estimation. Then, the image blocks are clustered using a k-means clustering based on the similarity of local blur kernels to generate blur type invariant regions. Finally, blur types of the regions are classified into out-of-focus or motion blur using a minimum distance classifier. The experimental results show that the proposed method successfully detects and classifies the regions blur types which outperforms the state-of-the-art techniques. Our proposed approach is used to detect inconsistency in the partial blur types of an image as an evidence of image tampering.

show abstract

Tamper Detection Based on Regularity of Wavelet Transform Coefficients

Cited by 43 publications

References 18 publications

A bibliography on blind methods for identifying image forgery

A bibliography on blind methods for identifying image forgery

Splicing detection in out-of-focus blurred images

Image tampering detection by exposing blur type inconsistency

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