Image splicing forgery detection based on low-dimensional singular value decomposition of discrete cosine transform coefficients

Moghaddasi, Zahra; Jalab, Hamid A.; Noor, Rafidah Md

doi:10.1007/s00521-018-3586-y

Cited by 20 publications

(7 citation statements)

References 27 publications

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“…Moghaddasi et al (2018) [ 1 ] used a low-dimensional singular value (SV) decomposition of the DCT coefficients to detect image splicing by computing the roughness measure of SVs. The dimension feature reduction was applied using the kernel PCA.…”

Section: Methodsmentioning

confidence: 99%

“…The method of Zhao et al [ 5 ] has the lowest accuracy with a dimensionality of 60. Although the algorithm of Moghaddasi et al [ 1 ] has the highest accuracy with 60 dimension features, this approach applied the kernel PCA for feature reduction. The high accuracy using the Cr color space that reached 99.50% without feature reduction is the strength of the proposed method compared with the other methods using the CASIA v2 image dataset [ 26 ].…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…Image forgery detection refers to the process of identifying inconsistent regions in an image to authenticate the input digital image [ 1 , 2 ]. The detection of image forgery is divided into two types [ 3 , 4 ], namely active and passive authentication.…”

Section: Introductionmentioning

confidence: 99%

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New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Jalab

Subramaniam

Ibrahim

et al. 2019

Entropy

Self Cite

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Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.

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Section: Methodsmentioning

confidence: 99%

Section: Comparison With Other Methodsmentioning

confidence: 99%

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New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Jalab

Subramaniam

Ibrahim

et al. 2019

Entropy

Self Cite

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“…References [20][21][22][23] propose using pixel replacement or pixel value substitution to encrypt image content. References [24][25][26][27][28] encrypt the image by changing the transform coefficient of the image in the frequency domain. Similar to the problems of anonymization technology, data encryption technology will also make corresponding assumptions for attacks and then design the corresponding encryption algorithm based on these assumptions.…”

Section: Introductionmentioning

confidence: 99%

Face Image Publication Based on Differential Privacy

Liu

Yang

Zhao

et al. 2021

Wireless Communications and Mobile Computing

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As an information carrier, face images contain abundant sensitive information. Due to its natural weak privacy, direct publishing may divulge privacy. Anonymization Technology and Data Encryption Technology are limited by the background knowledge and attack means of attackers, which cannot completely content the needs of face image privacy protection. Therefore, this paper proposes a face image publishing SWP (sliding window publication) algorithm, which satisfies the differential privacy. Firstly, the SWP translates the image gray matrix into a one-dimensional ordered data stream by using image segmentation technology. The purpose of this step is to transform the image privacy protection problem into the data stream privacy protection problem. Then, the sliding window model is used to model the data flow. By comparing the similarity of data in adjacent sliding windows, the privacy budget is dynamically allocated, and Laplace noise is added. In SWP, the data in the sliding window comes from the image. To present the image features contained in the data more comprehensively and use the privacy budget more reasonably, this paper proposes a fusion similarity measurement EM (exact mechanism) mechanism and a dynamic privacy budget allocation DA (dynamic allocation) mechanism. Also, for further improving the usability of human face images and reducing the impact of noise, a sort-SWP algorithm based on the SWP method is proposed in the paper. Through the analysis, it can be seen that ordered input can further improve the usability of the SWP algorithm, but direct sorting of data will destroy the ε -differential privacy. Therefore, this paper proposes a sorting method-SAS method, which satisfies the ε -differential privacy; SAS obtain an initial sort by using an exponential mechanism firstly. And then an approximate correct sort is obtained by using the Annealing algorithm to optimize the initial sort. Compared with LAP algorithm and SWP algorithm, the average accuracy rate of sort-SWP algorithm in ORL, Yale is increased by 56.63% and 21.55%, the recall rate is increased by 6.85% and 3.32%, and F1-sroce is improved by 55.62% and 16.55%.

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“…Finally, the image splicing tampering refers to a forged digital image created by fusing different region(s) from a couple or more images together. When manipulation expertly executed the spliced regions on the forged image is visually undetectable [5,6]. Image tampering can be detected using active and passive authentication methods.…”

Section: Introductionmentioning

confidence: 99%

Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

et al. 2019

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The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region’s boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region’s boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods.

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Image splicing forgery detection based on low-dimensional singular value decomposition of discrete cosine transform coefficients

Cited by 20 publications

References 27 publications

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Face Image Publication Based on Differential Privacy

Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

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