Detection and localization of forgery using statistics of DCT and Fourier components

Dua, Shilpa; Singh, Jyotsna; Parthasarathy, Harish

doi:10.1016/j.image.2020.115778

Cited by 26 publications

(13 citation statements)

References 24 publications

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“…The following points can be observed from Table 3: a) The CMFD that proposed in [26], [36], [40], are very robust to most of post-processing operations.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 94%

“…In general, most prevalent feature extraction methods for block-based methods is Frequency transform. The two primary reasons are noise robustness and rotational and translational components separability [36]. Techniques based on transformation include converting the original image data from a spatial domain (i.e.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

“…e) In [37], only geometric transformation are applied. f) In [36], only post processing operations are applied. g) The CMFD that proposed in [26], are very robust to post-processing operations and geometric transformation.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

“…However, it fail for other operations especially contrast change. b) The CMFD that proposed in [37], [26], [36], [38] are very robust to most of the geometric transformation. However, it fail for other geometric transformation such as translation.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

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A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Ahmed

Hammad

Jamil

2021

IJEECS

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<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>

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“…The following points can be observed from Table 3: a) The CMFD that proposed in [26], [36], [40], are very robust to most of post-processing operations.…”

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 94%

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

Section: Ic-mfd Algorithms Based Transform Domainmentioning

confidence: 99%

See 2 more Smart Citations

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Ahmed

Hammad

Jamil

2021

IJEECS

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“…Splicing detection [5][6][7][8][9][10] can determine whether a given image is authentic or tampered. In practical forensic applications, localizing splicing regions [11][12][13] compared with splicing detection is more effective.…”

Section: Introductionmentioning

confidence: 99%

Image Tampering Localization Using Demosaicing Patterns and Singular Value Based Prediction Residue

2021

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Almost all image sensors measure only one color per pixel through the color filter array. Missing pixels are estimated using a demosaicing process. For this reason, a demosaiced image leaves a particular trace. When an image is manipulated or tampered, the demosaicing trace can be changed. This change can serve as a basic clue for detecting or localizing image tampering. Demosaicing pattern-based tampering localization algorithms require a re-interpolation process, and the prediction residue between the given image and the re-interpolated image is commonly used to localize tampered regions. However, the prediction residue is not always valid because the demosaicing interpolation kernel cannot be known, which deteriorates the localization performance. This paper presents an effective re-interpolation process using singular value decomposition for an unknown demosaicing method. First, the green channel of the given image is decomposed into four sub-images according to the Bayer pattern. For a small block of each subimage, the singular value decomposition is performed. The prediction residue is obtained by reconstructing the image block after removing the largest singular value. The feature to localize the forged regions is extracted by the logarithm ratio of the prediction residue variance. The proposed method does not require any statistical model for the extracted feature, because the prediction residue is more accurate than that of conventional methods. We perform intensive experiments for three test datasets and compare the proposed method with state-of-the-art tampering localization methods, the results of which indicate that the proposed scheme outperforms existing approaches.INDEX TERMS Image tampering localization, demosaicing trace, singular value decomposition, prediction residue, re-interpolation kernel, color filter array, image splicing.

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Near duplicate detection of images with area and proposed pixel‐based feature extraction

Governor

Ramanujam²,

Mana

et al. 2022

Concurrency and Computation

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Efficient and accurate near-duplicate recognition is the trendy research area. Identification of invalid near-duplicate images offers a wide range of applications, including digital picture forensics, web-scale retrieval, and, social media analysis. This article intends to introduce a novel near duplicate detection model of images that consists of two stages such as (i) feature extraction and (ii) similarity computation. Originally, the image database is subjected to extracting the features, in which the area-based features and pixel-based features are extracted. Here, the area-based feature extraction includes the contrast context histogram (CCH-descriptors) and improved weighted bag of visual word (w-BovW) features; the pixel-based feature extraction includes the texture features like the proposed local vector pattern. Once the query image is given as the input, it is subjected to the feature extraction stage. Then, the feature vector database and the extracted features of query images are evaluated under similarity computation via improved Jaccard similarity evaluation. Thus, the near duplicate detection of the image is obtained in an effective manner. K E Y W O R D Sfeature extraction, feature vector database, local vector pattern, near duplicate detection, similarity computation INTRODUCTIONNowadays, digital contents are widespread and are redistributed easily, it may be lawful or unlawful. For instance, travelers, who travel to many places, take pictures, write travelogues and post them on the internet, these pictures are taken with or without viewpoint variation and that may be the shot from the same place. These images posted on the internet can be modified by other web users and they may repost them as their version.This generates a near-duplicate image. The near duplicate images are modified with the descriptions of normal images created through compression, geometric alterations, content augmentation, blurring, noise pollution, retaining and cutting out parts, and other techniques. The presence of those near-duplicate images causes wastage of storage, computing, and transmission resources, and it also affects the performance of the search engines critically, as the user has to search among the huge amount of near-duplicate images, for finding the image they want. Near-duplication of images 1,2 leads to wastage of resources in the network and it might be an indication of illegal transactions like image copyright violation. As a result, in web content security and image management, effective and efficient near-duplicate detection (NDD) of the picture is critical. Extracting useful features of an image to increase recognition accuracy is one of the key problems in near duplication image recognition. 3,4 Another issue is to increase the detection efficiency, which is difficult while considering the large image databases.The development of efficient and accurate NDD algorithms for various types of material such as video, image, text, and speech is a hot issue in research. 5,6 Near duplicates plays a crucial...

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Detection and localization of forgery using statistics of DCT and Fourier components

Cited by 26 publications

References 24 publications

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

Image Tampering Localization Using Demosaicing Patterns and Singular Value Based Prediction Residue

Near duplicate detection of images with area and proposed pixel‐based feature extraction

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