Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image

Sadeghi, Somayeh; Jalab, Hamid A.; Wong, KokSheik; Uliyan, Diaa M.; Dadkhah, Sajjad

doi:10.22452/mjcs.vol30no2.4

Cited by 21 publications

(16 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(12) Compute median absolute deviation (MAD) for all HGP-2s of the two matched image regions, and save it in feature vector f v . (13) Find the Euclidean distance between two corresponding final feature vectors fv and fv'. (14) Detect and localize the tampered regions.…”

Section: Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

“…The forgers perform duplicate regions with different geometric and post-processing operations to hide traces and make consistency with surrounding area [11][12][13].…”

mentioning

confidence: 99%

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

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the way of dividing the image on the second stage of copy-move forgery detection, these techniques are classified into three classes: block-based schemes [34], segmented regions-based schemes [41] and local keypoints based schemes [38]. In the block-based, the image is divided into a number of sub-blocks either square blocking or circle blocking.…”

Section: Related Workmentioning

confidence: 99%

“…Conversely, some scientific papers have examined the robustness against the retouching or blending tools which hide visual editing artifacts in the image through some post-processing attacks. Such attacks include: blurring [43,46], additive noise [38] and JPEG compression [19,42] impacts are obtained after applying geometrical transformation operations. Hence, this type of forgery is a challenging problem that motivates us to investigate forged images against scale, rotation and blur attacks.…”

Section: Introductionmentioning

confidence: 99%

Blur Invariant Features For Exposing Region Duplication Forgery Using ANMS And Local Phase Quantization

Uliyan¹,

Abushariah²,

Altamimi³

2018

IJCAI

Self Cite

View full text Add to dashboard Cite

In digital image forensics, local interest points can be employed to faithfully detect region duplication forgery. Authentic images may be abused by copy-move forgery to fully contained duplicated regions such as objects. Recent existing local interest point forgery detection methods fail to detect this type of forgery in the retouched regions by some geometric transformations. To solve this challenge, local interest points should be detected which cover all the regions with high primitives like corners and edges. These primitives represent the internal structure of any object in the image which makes them have a discriminating property under geometric transformations such as scale and rotation operation. They can be exposed based on Scale-Invariant Features Transform (SIFT) algorithm. Here, we provide an image forgery detection technique by using local interest points. First, the image is segmented based on fuzzy C means to divide the image into homogenous regions that have the same texture. Second, local interest points are exposed by extracting Adaptive non-maximal suppression (ANMS) from dividing blocks in the segmented image to detect such corners of objects. We also demonstrate that ANMS Keypoints can be effectively utilized to detect blurred and scaled forged regions. The ANMS features of the image are shown to exhibit the internal structure of copy moved region. We provide a new texture descriptor called local phase Quantization (LPQ) that is robust to image blurring and also to eliminate the false positives of duplicated regions. Experimental results show that our scheme has the ability to reveal region duplication forgeries under scaling, rotation and blur manipulation of JPEG images on MICC-F220 and CASIA v. 2 Image Datasets. Povzetek: Predstavljena je izvirna metoda za odkrivanje ponarejenih področij v sliki.

show abstract

“…Discovering copy-move tampering is more challenging because there are no significant visible alterations in the forged image texture. Various researchers are actively exploring in term of the Key-Points and Blockbased approach [4]. Finally, the image splicing tampering refers to a forged digital image created by fusing different region(s) from a couple or more images together.…”

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

View full text Add to dashboard Cite

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.

show abstract

Keypoint Based Authentication And Localization Of Copy-Move Forgery In Digital Image

Cited by 21 publications

References 27 publications

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

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

Blur Invariant Features For Exposing Region Duplication Forgery Using ANMS And Local Phase Quantization

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

Contact Info

Product

Resources

About