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

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1392
Author(s):  
Subramaniam ◽  
Jalab ◽  
Ibrahim ◽  
Mohd Noor
Keyword(s):  
Wavelet Transform ◽  
Fractional Calculus ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Splicing ◽  
Information Carrier ◽  
Image Splicing Detection ◽  
Splicing Detection ◽  
Redundant Discrete Wavelet Transform

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.

scholarly journals Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Muhammad Hameed Siddiqi ◽  
Khurshed Asghar ◽  
Umar Draz ◽  
Amjad Ali ◽  
Madallah Alruwaili ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Image ◽  
Digital Images ◽  
Local Binary Patterns ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Image Forgery Detection

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

Real Time QRS Detection Based on Redundant Discrete Wavelet Transform

2016 ◽  
Vol 14 (4) ◽  
pp. 1662-1668 ◽  
Author(s):  
Ernano Arrais Junior ◽  
Ricardo Alexandro de Medeiros Valentim ◽  
Glaucio Bezerra Brandao
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Discrete Wavelet ◽  
Qrs Detection ◽  
Redundant Discrete Wavelet Transform

Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

2020 ◽  
pp. 61-79
Author(s):  
Ruxin Wang ◽  
Wei Lu ◽  
Jixian Li ◽  
Shijun Xiang ◽  
Xianfeng Zhao ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Color Image ◽  
Transition Probability ◽  
Ensemble Classifier ◽  
Image Splicing ◽  
Image Splicing Detection ◽  
Detection Approach ◽  
Quaternion Wavelet Transform ◽  
Splicing Detection ◽  
Markov Features

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this article, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. First, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real parts and three imaginary parts of QDCT coefficients, respectively. Then, additional Markov features are extracted from the luminance (Y) channel in the quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet sub-band coefficients. Finally, an ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

Multifocus image fusion based on coefficient significance of redundant discrete wavelet transform

2019 ◽  
Vol 46 (3) ◽  
pp. 377-383
Author(s):  
Jianhua Liu ◽  
Peng Geng ◽  
Hongtao Ma
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Fusion Rule ◽  
Discrete Wavelet ◽  
Wavelet Domain ◽  
Content Type ◽  
Ripplet Transform ◽  
Detail Coefficient ◽  
Redundant Discrete Wavelet Transform

Purpose This study aims to obtain the more precise decision map to fuse the source images by Coefficient significance method. In the area of multifocus image fusion, the better decision map is very important the fusion results. In the processing of distinguishing the well-focus part with blur part in an image, the edge between the parts is more difficult to be processed. Coefficient significance is very effective in generating the better decision map to fuse the multifocus images. Design/methodology/approach The energy of Laplacian is used in the approximation coefficients of redundant discrete wavelet transform. On the other side, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient. Findings Due to the shift-variance of the redundant discrete wavelet and the effectiveness of fusion rule, the presented fusion method is superior to the region energy in harmonic cosine wavelet domain, pixel significance with the cross bilateral filter and multiscale geometry analysis method of Ripplet transform. Originality/value In redundant discrete wavelet domain, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient of source images.

Deep Learning on Digital Image Splicing Detection Using CFA Artifacts

2020 ◽  
Vol 12 (2) ◽  
pp. 31-44 ◽  
Author(s):  
Nadheer Younus Hussien ◽  
Rasha O. Mahmoud ◽  
Hala Helmi Zayed
Keyword(s):  
Digital Image ◽  
Feature Reduction ◽  
Color Filter ◽  
Forgery Detection ◽  
Image Splicing ◽  
Detection Scheme ◽  
Image Forgery ◽  
Image Splicing Detection ◽  
Splicing Detection ◽  
Digital Image Forgery

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.

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