Image forgery detection using steerable pyramid transform and local binary pattern

Muhammad, Ghulam; Al-Hammadi, Muneer; Hussain, Muhammad; Bebis, George

doi:10.1007/s00138-013-0547-4

Cited by 174 publications

(84 citation statements)

References 13 publications

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“…The results of The italicized data present the maximum detection accuracy the other methods were taken from the corresponding literatures and the best detection accuracies are presented. The method in [17] indicates marginally greater accuracies for the Columbia color and CASIA2 datasets than those of the proposed method. However, the number of features is greater than that for the proposed algorithm, and the features are varied according to the image datasets.…”

Section: Detection Results For Color Datasetsmentioning

confidence: 88%

“…Recently, splicing direction methods applicable to color datasets are presented [16][17][18][19]. An image splicing detection algorithm using the run-length run-number and kernel PCA was presented [16].…”

Section: Introductionmentioning

confidence: 99%

“…However, splicing detection methods for color images should test for three color channels, and then, select one color channel which has the best detection accuracy. Muhammad et al proposed an imposing image forgery detection method based on a steerable pyramid transform and local binary pattern with feature reduction [17]. This method demonstrated the best performance to rate of 97.33 % on CASIA2 dataset [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain

Park

Han

Moon

et al. 2016

J Image Video Proc.

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In this paper, we propose an image splicing detecting method using the characteristic function moments for the inter-scale co-occurrence matrix in the wavelet domain. We construct the co-occurrence matrices by using a pair of wavelet difference values across inter-scale wavelet subbands. In this process, we do not adopt the thresholding operation to prevent information loss. We extract the high-order characteristic function moments of the two-dimensional joint density function generated by the inter-scale co-concurrent matrices in order to detect image splicing forgery. Our method can be applied regardless of the color or gray image dataset using only luminance component of an image. By performing experimental simulations, we demonstrate that the proposed method achieves good performance in splicing detection. Our results show that the detection accuracy was greater than 95 % on average with well-known four splicing detection image datasets.

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Section: Detection Results For Color Datasetsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain

Park

Han

Moon

et al. 2016

J Image Video Proc.

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“…The wavelet transform function is basically being a combination of lower and high frequency. The process of sampling of original image and forged image are describing here [13,14].…”

Section: Feature Extractionmentioning

confidence: 99%

Digital Image Forgery Detection based on Texture Feature and Clustering Technique

Ujjainiya¹,

Chugh²

2016

IJCA

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The image forgery detection is important tools in digital multi-media analysis. Now a day's digital multi-media faced a problem of copy paste and tampering by different multi-media authoring tools. The tampered and copy paste image change the actual scenario of original image and its illegal process in current scenario of multi-media. For the detection of image forgery various pixel and transform based method are applied. The applied method is better in some detection and estimation, but faced a certain limitation. In this paper proposed texture based image forgery detection. The texture based image forgery detection is very efficient in terms of detection ratio. For the extraction of texture feature used discrete wavelet transform function. For the generation of block used partition clustering technique. the partition clustering technique creates the block of original and forged image. The proposed algorithm is simulated in MATLAB software and used very famous dataset MFIC2000.

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“…In image splicing, the copied region and the pasted region belongs to a different images. The purpose of the image forgery is to duplicate or conceal a certain object into an image or to make false propaganda 4 . Forging an image is usually accompanied by post-processing operations like JPEG compression, adding noise and image blurring or geometric operations such as scaling, shifting and rotation increases the detection tasks difficult.…”

mentioning

confidence: 99%

Image Forgery Detection Based on Gabor Wavelets and Local Phase Quantization

Isaac

Wilscy

2015

Procedia Computer Science

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Image Forgery detection has become a hot area of research as a result of the increasing number of forged images circulating around in the Internet and other social media and due to the legal and social issues that they are creating. The key problem faced by the researchers is to categorize an image as forged or authentic and to localize the forgery. Several methods were proposed, but a proper method which can accurately detect image forgery is yet to be invented. Here, we propose a novel image forgery detection technique by taking texture information of the image as a distinguishing feature. The method relies on Gabor wavelets and Local Phase Quantization (LPQ) which can extract relevant texture features which are fed as input to a Support Vector Machine for classification. The results indicate an accuracy of over 99% on both CASIA v1 and the DVMM color dataset. It outperforms similar state-of-art methods in solving image forgery detection.

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Image forgery detection using steerable pyramid transform and local binary pattern

Cited by 174 publications

References 13 publications

Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain

Image splicing detection based on inter-scale 2D joint characteristic function moments in wavelet domain

Digital Image Forgery Detection based on Texture Feature and Clustering Technique

Image Forgery Detection Based on Gabor Wavelets and Local Phase Quantization

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