Detection of copy–move image forgery using histogram of orientated gradients

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

doi:10.1016/j.ins.2015.03.009

Cited by 122 publications

(47 citation statements)

References 14 publications

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“…The detection performance of the proposed CMFD is compared with that of the state-ofthe-art techniques in literature that used the same CoMoFoD dataset and validation metrics to achieve fair comparison. Table 8 presents a comparison of the proposed approach with other popular approaches, namely, HOG [3], HOGM [39], PCET [40], LGWP [41] and Convolutional Kernel Network [42]. The proposed CMFD based on QPCET descriptors provide superior detection efficiency to previous methods.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, digital forgery detection is introduced to address this issue and emerges as an important field in image processing [2]. Digital forgery detection techniques are classified into active and passive (blind) [3]. Active approach embeds data or digital signature into original images via pre-processing, thereby limiting its practical applications.…”

Section: Introductionmentioning

confidence: 99%

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Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

2019

KSII TIIS

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Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis. CMF is performed by copying and pasting a part of an image into other portions of it. Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult. A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks. Secondly, invariant features for each block are extracted using QPCET. Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching. Lastly, a new technique is introduced to reduce the flat region-mediated false matches. Experiments are performed on numerous images selected from the CoMoFoD database. MATLAB 2017b is used to employ the proposed method. Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method. Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression. Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate. The proposed approach may serve as a basis for accurate digital image forensic investigations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

2019

KSII TIIS

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“…Hence, a large majority of image forgery detection methods adopt a passive-based strategy to perform the type of tampering identification discussed in the present study. Passive detection technology can be categorized into blockbased methods [2][3][4][5][6][7][8][9][10] and keypoint-based methods [11][12][13][14][15][16][17][18][19][20][21]. In the present study, we focus on the keypointbased approach.…”

Section: Introductionmentioning

confidence: 99%

“…This method is time consuming and does not detect any rotation angles for duplication regions. Lee et al [9] used a histogram of oriented gradients (HOG) of each block as features; these features are ordered by using lexicographical sorting. The duplicated image blocks are detected by measuring similar block pairs.…”

Section: Introductionmentioning

confidence: 99%

“…The duplicated image blocks are detected by measuring similar block pairs. Li et al [10] used a polar harmonic transform to extract the rotation and scaling invariant features as block features (similar to the method of Lee et al [9]). These feature vectors are lexicographically sorted, and the forged regions are detected by finding similar block pairs.…”

Section: Introductionmentioning

confidence: 99%

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Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation

Huang

Ciou

2019

J Image Video Proc.

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The increasing popularity of the internet suggests that digital multimedia has become easier to transmit and acquire more rapidly. This also means that this multimedia has become more susceptible to tampering through forgery. One type of forgery, known as copy-move duplication, is a specified type that usually involves image tampering. In this study, a keypoint-based image forensics approach based on a superpixel segmentation algorithm and Helmert transformation has been proposed. The purpose of this approach is to detect copy-move forgery images and to obtain forensic information. The procedure of the proposed approach consists of the following phases. First, we extract the keypoints and their descriptors by using a scale-invariant feature transform (SIFT) algorithm. Then, based on the descriptor, matching pairs will be obtained by calculating the similarity between keypoints. Next, we will group these matching pairs based on spatial distance and geometric constraints via Helmert transformation to obtain the coarse forgery regions. Then, we refine these coarse forgery regions and remove mistakes or isolated areas. Finally, the forgery regions can be localized more precisely. Our proposed approach is a more robust solution for scaling, rotation, and compression forgeries. The experimental results obtained from testing different datasets demonstrate that the proposed method can obtain impressive precision/ recall rates in comparison to state-of-the-art methods.

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Squirrel Henry Gas Solubility Optimization driven Deep Maxout Network with multi‐texture feature descriptors for digital image forgery detection

Nirmala Priya,

Suresh Kumar,

Suganthi

et al. 2024

Concurrency and Computation

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SummaryThe ability to detect image forgeries is a crucial component in solving many problems, especially social ones that arise in legal proceedings, on Facebook, and so forth. Generally, forgery detection attempts to predict the artifacts through evaluating the differences in texture properties of an image. One division of an image is copied in a comparable image, typically at a different position, in a process known as copy move forgery. The two main types of copy move forgery prediction are keypoint‐based and block‐based techniques. While the block‐based approach takes more time, the keypoint‐based model performs poorly. The goal of this research was to develop a method for detecting image counterfeiting called the Squirrel Henry Gas Solubility Optimization algorithm‐based Deep Maxout Network (SHGSO‐based DMN). Here, Steerable Pyramid Transform (SPT) decomposition is done in order to obtain quantity of multi‐scale and multi‐oriented sub bands. For the purpose of detecting forgeries, the Deep Maxout Network which was constructed using the SHGSO model is deployed. With accuracy of 0.9507, True Negative Rate (TNR) of 0.9568, and True Positive Rate (TPR) of 0.9517, the new method outperformed other current forgery detection techniques.

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Detection of copy–move image forgery using histogram of orientated gradients

Cited by 122 publications

References 14 publications

Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation

Squirrel Henry Gas Solubility Optimization driven Deep Maxout Network with multi‐texture feature descriptors for digital image forgery detection

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