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

Uliyan, Diaa M.; Jalab, Hamid A.; Wahab, Ainuddin Wahid Abdul; Sadeghi, Somayeh

doi:10.3390/sym8070062

Cited by 34 publications

(23 citation statements)

References 34 publications

(56 reference statements)

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“…The dimension of descriptor is usually 64 and 128 for its extended counterpart. In [16], harris corner points are employed with uniform distribution all over in order to attain an adequate number of feature points. In case of BRISK [17] features, the rotation invariance is achieved by identifying characteristic direction of each keypoint.…”

Section: Related Workmentioning

confidence: 99%

Calibrating Thresholds Based on Trade-Offs Between Detection Accuracy and FPR for Copy Move Forgery Detection

Walia¹,

Kumar²

2019

IJRTE

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In this paper, prominent keypoint based features are compared in order to analyze their reliability and efficiency against forgery detection. Four features specifically SURF, KAZE, Harris corner points and BRISK features are used individually on a set of images. The method includes four phases: Image pre-processing, keypoint detection, feature vector description and feature vector matching. In feature matching, MaxRatio has been chosen as a varying parameter for calculating values of false positives and false negatives for each feature. MaxRatio defines the ratio for rejecting ambiguous matches of feature descriptors in the images. The optimal threshold value for MaxRatio is calibrated with the help of trade-off between detection accuracy and false positive ratio. The changes in false negative ratio and false positive ratio are picturized in order to find out optimal threshold for detection accuracy. ROC curves are also plotted for each feature at different values of MaxRatio and area under the ROC curves are calculated. The experiments are performed on two benchmark datasets, namely CASIA version 2.0 and MICC-F600. It has been perceived from experimental outcomes that KAZE features gave best values for all the performance metrics namely accuracy, precision, area under the ROC curve and F1-score with little compromise in time complexity, whereas Harris corner points gave the worst results as compared to rest of the features. Further, in order to improve the execution time, the computation of non-linear scale space process in KAZE can be simplified and GPU programming for real-time performance may also be used.

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Section: Related Workmentioning

confidence: 99%

Calibrating Thresholds Based on Trade-Offs Between Detection Accuracy and FPR for Copy Move Forgery Detection

Walia¹,

Kumar²

2019

IJRTE

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“…The Harris corners algorithm employs as key-points based method for detect the forgery in the region. In addition, Harris corner detector algorithm determine static features to geometric transformations, where its used as a key-points operation in order to represent the internal structure of identical image areas [9]. Also, Harris corner detector algorithm is an invariant rotation technique that determines the corners of the image [10].…”

Section: Harris Algorithmmentioning

confidence: 99%

“…"Harris corner detection is based on the second moment auto correlation matrix. This matrix describes the Gradient distribution of input images at point x, weighted by Gaussian G(x,σ) as the following [9]:…”

Section: Harris Algorithmmentioning

confidence: 99%

Detection and Feature Extraction for Images Signatures

Mohamad¹,

Alsuhimat²,

Mohamed³

et al. 2018

IJET

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The signing process is one of the most important processes used by organizations to ensure the confidentiality of information and to protect it against any unauthorized penetration or access to such information. As organizations and individuals enter the digital world, there is an urgent need for a digital system capable of distinguishing between the original and fraud signature, in order to ensure individuals authorization and determine the powers allowed to them. In this paper, three widely used feature detection algorithms, HARRIS, BRISK (Binary Robust Invariant Scalable Keypoints) and FAST (Features from Accelerated Segment), these algorithms are compared to calculate the run time and accuracy for set of signature images. Three techniques have been applied using (UTSig) dataset; the experiment consisted of four phases: first, applying the techniques on one image, then on four images, then on eight images, finally applying the techniques on ten images where time and accuracy were calculated for each algorithm in the all phases. The results showed that the BRISK algorithm got the best result among the feature detection algorithm in terms of accuracy and the FAST algori thm got the best result among the feature detection algorithm in terms of run time.

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“…It virtually impacts every facet of our society. Image forgeries can be achieved through using various techniques such as copy-move and splicing methods as shown in Figure 1 [1]. Copy-move refers to a forgery where a region(s) from an image is cloned in the same original image.…”

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

Cited by 34 publications

References 34 publications

Calibrating Thresholds Based on Trade-Offs Between Detection Accuracy and FPR for Copy Move Forgery Detection

Calibrating Thresholds Based on Trade-Offs Between Detection Accuracy and FPR for Copy Move Forgery Detection

Detection and Feature Extraction for Images Signatures

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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