SVD-based image splicing detection

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

doi:10.1109/icimu.2014.7066598

Cited by 19 publications

(13 citation statements)

References 14 publications

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“…Both the method makes an alteration in image smoothness, regularity, continuity and periodicity. Hence, statistical approaches are applied to detect these traces [7]. As steganalysis and Image Splicing detection make use of statistical approaches, some of the statistical natural models applied for steganalysis can be applied for Image Splicing detection.…”

Section: Accents Transactions On Information Security Vol 2(5)mentioning

confidence: 99%

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Digital image forgery detection techniques: a survey

Shwetha¹,

Sathyanarayana²

2016

ATIS

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Section: Accents Transactions On Information Security Vol 2(5)mentioning

confidence: 99%

“…Four general methods applied for steganalysis were applied to image splicing detection with the accuracy of less than 80% [7].…”

Section: Accents Transactions On Information Security Vol 2(5)mentioning

confidence: 99%

Digital image forgery detection techniques: a survey

Shwetha¹,

Sathyanarayana²

2016

ATIS

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“…(7) Apply angular radial partitioning (ARP) on each image region indexed by its centroid coordinates in the same cluster. (8) Convert input image into grayscale and extract key points with Harris detectors. (9) Calculate the total number of Harris corners in each sector of a circle image region in the same cluster.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Image splicing adds a part of an image into another image in order to hide or change the content of the second image [6][7][8][9].…”

mentioning

confidence: 99%

Image Region Duplication Forgery Detection Based on Angular Radial Partitioning and Harris Key-Points

et al. 2016

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Abstract:Region duplication forgery where a part of the image itself is copied and pasted onto a different part of the same image grid is becoming more popular in image manipulation. The forgers often apply geometric transformations such as rotation and scaling operations to make the forgery imperceptible. In this study, an image region duplication forgery detection algorithm is proposed based on the angular radial partitioning and Harris key-points. Two standard databases have been used: image data manipulation and MICC-F220 (Media Integration and Communication Center-of the University of Florence) for experimentation. Experiment results demonstrate that the proposed technique can detect rotated regions in multiples of 30 degrees and can detect region duplication with different scaling factors from 0.8, to 1.2. More experimental results are presented to confirm the effectiveness of detecting region duplication that has undergone other changes, such as Gaussian noise, and JPEG compression.

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“…Each image acquisition device leaves some non-modifiable, noise-like signal, which can be used to detect the forgery. The passive techniques are classified as pixel based techniques, i.e., copy-move [11,12], image splicing [13], and image retouching [14][15][16][17][18][19][20][21][22], format based techniques [23][24][25][26], camera based techniques [27,28], physical based techniques [29][30][31][32][33][34][35], and geometric based techniques [36,37]. These techniques are computationally expensive and time consuming and are not commonly used.…”

Section: Introductionmentioning

confidence: 99%

Forgery Detection and Localization of Modifications at the Pixel Level

Khan

Ali

et al. 2020

Symmetry

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In this paper, we present a new technique of image forgery detection. The proposed technique uses digital signatures embedded in the least significant bits of the selected pixels of each row and column. The process maintains a symmetry in the use of pixels for computing and hiding the digital signatures. Each row and column of the image symmetrically contributes to both processes, with the number of pixels per row or column used for computing the signature, and the pixels used for embedding are not equal and are asymmetric. The pixels in each row and column of an image are divided into two groups. One group contains pixels of a row or column used in the calculation of digital signatures, and the second group of pixels is used for embedding the digital signatures of the respective row or column. The digital signatures are computed using the hash algorithm, e.g., message digest five (MD5). The least significant bits substitution technique is used for embedding the computed digital signature in the least significant bits of the selected pixels of the corresponding row or column. The proposed technique can successfully detect the modification made in an image. The technique detects pixel level modification in a single or multiple pixels.

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SVD-based image splicing detection

Cited by 19 publications

References 14 publications

Digital image forgery detection techniques: a survey

Digital image forgery detection techniques: a survey

Image Region Duplication Forgery Detection Based on Angular Radial Partitioning and Harris Key-Points

Forgery Detection and Localization of Modifications at the Pixel Level

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