Improved DCT-based detection of copy-move forgery in images

Huang, Yanping; Lu, Wei; Sun, Wei; Long, Dang Xuan

doi:10.1016/j.forsciint.2010.08.001

Cited by 265 publications

(136 citation statements)

References 8 publications

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“…A variety of dimension reduction techniques e.g. DWT, DCT, DFT, PCA and SVD are presented in the literature to reduce the computational complexity of the algorithm [12][13][14][15].…”

Section: Proposed Methodologymentioning

confidence: 99%

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Block and Fuzzy Techniques Based Forensic Tool for Detection and Classification of Image Forgery

Hashmi¹,

Keskar²

2015

Journal of Electrical Engineering and Technology

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-In today's era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the 'non-expert' forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

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“…A variety of dimension reduction techniques e.g. DWT, DCT, DFT, PCA and SVD are presented in the literature to reduce the computational complexity of the algorithm [12][13][14][15].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…In this regard Huang et al [15] has proposed improved DCT based image forgery detection system which is capable of to cope up with distortions like blurring, JPEG compression and additive white Gaussian noise.…”

Section: Related Workmentioning

confidence: 99%

Block and Fuzzy Techniques Based Forensic Tool for Detection and Classification of Image Forgery

Hashmi¹,

Keskar²

2015

Journal of Electrical Engineering and Technology

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“…For a threshold value of , the method judges the similarity between two vectors as given in Eq. (6).…”

Section: The Proposed Algorithm Based On Lpqmentioning

confidence: 99%

“…Their work used sum of angle values to achieve rotation invariance. In 2011, Huang et al improved the performance of the Fridrich's method by reducing the dimension of feature vector [6]. Their method divides the image into overlapping blocks and extract feature vectors from the blocks using DCT as in Ref.…”

Section: Introductionmentioning

confidence: 99%

Blur Invariant Image Forgery Detection Method Using Local Phase Quantization

Üstübi̇oğlu¹,

Baykal²,

Muzaffer³

et al. 2016

JEPE

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Abstract:With the rapid development of powerful image, editing software makes the forgery of the digital image easy. Researchers proposed methods to cope with image authentication in recent years. We proposed a passive image authentication technique to determine the copy move forgery that copied a part of an image and pasted it on the other region in the same image. First, the method divides the image into overlapping blocks. It uses LPQ (local phase quantization) to label each block. The column average value of labeled blocks constitutes the feature vector for the block. Similarity among the feature vectors gives a clue about the forgery. Local phase quantization has not been used to detect copy move forgery in the literature before. Experimental results show that, the method has higher accuracy ratios and lower false negative values under blurring operation at high levels compared to other methods. Our method can also detect multiple copy move forgery.

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“…An improved DCT (discrete cosine transform)-based technique was proposed in [2] to discover CMF in digital images. The image is subdivided into blocks, and the DCT is computed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Image Forgery Detection Using Multi-scale Weber Local Descriptors

Saleh

Hussain

Muhammad

et al. 2013

Advances in Visual Computing

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Abstract. In this paper, a detailed evaluation of multi-scale Weber local descriptors (WLD) based image forgery detection method is presented. Multiscale WLD extracts the features from chrominance components of an image, which usually encode the tampering information that escapes the human eyes. The WLD incorporates differential excitation and gradient orientation of a center pixel around a neighborhood. In the multi-scale WLD, three different neighborhoods are chosen. A support vector machine is used for classification purpose. The experiments are conducted on three image databases, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results show that the accuracy rate of the proposed method are 94.19% for CASIA v1.0, 96.61% for CASIA v2.0, and 94.17% for Columbia dataset. These accuracies are significantly higher than those obtained by some state-of-the-art methods.

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Improved DCT-based detection of copy-move forgery in images

Cited by 265 publications

References 8 publications

Block and Fuzzy Techniques Based Forensic Tool for Detection and Classification of Image Forgery

Block and Fuzzy Techniques Based Forensic Tool for Detection and Classification of Image Forgery

Blur Invariant Image Forgery Detection Method Using Local Phase Quantization

Evaluation of Image Forgery Detection Using Multi-scale Weber Local Descriptors

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