Copy-Move Forgery Detection Using DyWT

2020 ◽  
pp. 117-126
Author(s):  
Choudhary Shyam Prakash ◽  
Sushila Maheshkar
Keyword(s):  
Wavelet Transform ◽  
Input Image ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
High Similarity ◽  
Dyadic Wavelet ◽  
Region Duplication ◽  
Dyadic Wavelet Transform ◽  
Copy Move Forgery Detection ◽  
Duplication Detection

In this paper, we proposed a passive method for copy-move region duplication detection using dyadic wavelet transform (DyWT). DyWT is better than discrete wavelet transform (DWT) for data analysis as it is shift invariant. Initially we decompose the input image into approximation (LL1) and detail (HH1) sub-bands. Then LL1 and HH1 sub-bands are divided into overlapping sub blocks and find the similarity between the blocks. In LL1 sub-band the copied and moved blocks have high similarity rate than the HH1 sub-band, this is just because, there is noise inconsistency in the moved blocks. Then we sort the LL1 sub-band blocks pair based on high similarity and in HH1 blocks are sorted based on high dissimilarity. Then we apply threshold to get the copied moved blocks. Here we also applied some post processing operations to check the robustness of our method and we get the satisfactory results to validate the copy move forgery detection.

Copy-Move Forgery Detection Using DyWT

2020 ◽  
pp. 741-750
Author(s):  
Choudhary Shyam Prakash ◽  
Sushila Maheshkar
Keyword(s):  
Wavelet Transform ◽  
Input Image ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
High Similarity ◽  
Dyadic Wavelet ◽  
Region Duplication ◽  
Dyadic Wavelet Transform ◽  
Copy Move Forgery Detection ◽  
Duplication Detection

In this paper, we proposed a passive method for copy-move region duplication detection using dyadic wavelet transform (DyWT). DyWT is better than discrete wavelet transform (DWT) for data analysis as it is shift invariant. Initially we decompose the input image into approximation (LL1) and detail (HH1) sub-bands. Then LL1 and HH1 sub-bands are divided into overlapping sub blocks and find the similarity between the blocks. In LL1 sub-band the copied and moved blocks have high similarity rate than the HH1 sub-band, this is just because, there is noise inconsistency in the moved blocks. Then we sort the LL1 sub-band blocks pair based on high similarity and in HH1 blocks are sorted based on high dissimilarity. Then we apply threshold to get the copied moved blocks. Here we also applied some post processing operations to check the robustness of our method and we get the satisfactory results to validate the copy move forgery detection.

Copy-Move Forgery Detection Using DyWT

2017 ◽  
Vol 8 (2) ◽  
pp. 1-9
Author(s):  
Choudhary Shyam Prakash ◽  
Sushila Maheshkar
Keyword(s):  
Wavelet Transform ◽  
Input Image ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
High Similarity ◽  
Dyadic Wavelet ◽  
Region Duplication ◽  
Copy Move Forgery Detection ◽  
Duplication Detection ◽  
Better Than

In this paper, we proposed a passive method for copy-move region duplication detection using dyadic wavelet transform (DyWT). DyWT is better than discrete wavelet transform (DWT) for data analysis as it is shift invariant. Initially we decompose the input image into approximation (LL1) and detail (HH1) sub-bands. Then LL1 and HH1 sub-bands are divided into overlapping sub blocks and find the similarity between the blocks. In LL1 sub-band the copied and moved blocks have high similarity rate than the HH1 sub-band, this is just because, there is noise inconsistency in the moved blocks. Then we sort the LL1 sub-band blocks pair based on high similarity and in HH1 blocks are sorted based on high dissimilarity. Then we apply threshold to get the copied moved blocks. Here we also applied some post processing operations to check the robustness of our method and we get the satisfactory results to validate the copy move forgery detection.

scholarly journals IMAGE FORGERY DETECTION USING DYADIC WAVELET TRANSFORM

2013 ◽  
pp. 108-110
Author(s):  
O.M. PRATHIBHA ◽  
N. S. SWATHIKUMARI ◽  
P. SUSHMA
Keyword(s):  
Wavelet Transform ◽  
Detection Method ◽  
Input Image ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Forgery ◽  
Dyadic Wavelet ◽  
Image Forgery Detection ◽  
Dyadic Wavelet Transform ◽  
The One

In this paper, we propose a blind copy move image forgery detection method using dyadic wavelet transform (DyWT). DyWT is shift invariant and therefore more suitable than discrete wavelet transform (DWT) for data analysis. First we decompose the input image into approximation (LL1) and detail (HH1) subbands. Then we divide LL1 and HH1 subbands into overlapping blocks and measure the similarity between blocks. The key idea is that the similarity between the copied and moved blocks from the LL1 subband should be high, while the one from the HH1 subband should be low due to noise inconsistency in the moved block. We sort pairs of blocks based on high similarity using the LL1 subband and high dissimilarity using the HH1 subband. Using thresholding, we obtain matched pairs from the sorted list as copied and moved blocks. Experimental results show the effectiveness of the proposed method over competitive methods using DWT and the LL1 or HH1 subbands only.

scholarly journals GENDER RECOGNITION FROM FACE IMAGES WITH DYADIC WAVELET TRANSFORM AND LOCAL BINARY PATTERN

2013 ◽  
Vol 22 (06) ◽  
pp. 1360018 ◽  
Author(s):  
MUHAMMAD HUSSAIN ◽  
IHSAN ULLAH ◽  
HATIM A. ABOALSAMH ◽  
GHULAM MUHAMMAD ◽  
GEORGE BEBIS ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Local Binary Pattern ◽  
Feature Subset Selection ◽  
Feature Descriptor ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Gender Recognition ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Lbp Descriptor

Gender recognition from facial images plays an important role in biometric applications. Employing Dyadic wavelet Transform (DyWT) and Local Binary Pattern (LBP), we propose a new feature descriptor DyWT-LBP for gender recognition. DyWT is a multi-scale image transformation technique that decomposes an image into a number of sub-bands which separate the features at different scales. DyWT is a kind of translation invariant wavelet transform that has a better potential for detection than Discrete Wavelet Transform (DWT). On the other hand, LBP is a texture descriptor and is known to be the best for representing texture micro-patterns, which play a key role in the discrimination of different objects in an image. For DyWT, we used spline dyadic wavelets (SDW). There exist many types of SDW; we investigated a number of SDWs for finding the best SDW for gender recognition. The dimension of the feature space generated by DyWT-LBP descriptor becomes excessively high. To tackle this problem, we apply a feature subset selection (FSS) technique that not only reduces the number of features significantly but also improves the recognition accuracy. Through a large number of experiments performed on FERET and Multi-PIE databases, we report for DyWT-LBP descriptor the parameter settings, which result in the best accuracy. The proposed system outperforms the stat of the art gender recognition approaches; it achieved a recognition rate of 99.25% and 99.09% on FERET and Multi-PIE databases, respectively.

TWO-DIMENSIONAL STATIONARY DYADIC WAVELET TRANSFORM, DECIMATED DYADIC DISCRETE WAVELET TRANSFORM AND THE FACE RECOGNITION APPLICATION

2011 ◽  
Vol 09 (03) ◽  
pp. 397-416 ◽  
Author(s):  
YANKUI SUN ◽  
YONG CHEN ◽  
HAO FENG
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Wavelet Filters ◽  
Dyadic Wavelet ◽  
Dyadic Wavelet Transform ◽  
Approximation Coefficients

Currently, two-dimensional dyadic wavelet transform (2D-DWT) is habitually considered as the one presented by Mallat, which is defined by an approximation component, two detail components in horizontal and vertical directions. This paper is to introduce a new type of two-dimensional dyadic wavelet transform and its application so that dyadic wavelet can be studied and used widely furthermore. (1) Two-dimensional stationary dyadic wavelet transform (2D-SDWT) is proposed, it is defined by approximation coefficients, detail coefficients in horizontal, vertical and diagonal directions, which is essentially the extension of two-dimensional stationary wavelet transform for orthogonal/biorthogonal wavelet filters. (2) ε-decimated dyadic discrete wavelet transform (DDWT) is introduced and its relation with 2D-SDWT is given, where ε is a sequence of 0's and 1's. (3) Mallat decomposition algorithm based on dyadic wavelet is introduced as a special case of ε-decimated DDWT, and so a face recognition algorithm based on dyadic wavelet is proposed, and experimental results are given to show its effectiveness.

Estimation of Heart Rate Variability Fluctuations by Wavelet Transform

2011 ◽  
Vol 57 (3) ◽  
pp. 395-400 ◽  
Author(s):  
Anton Popov ◽  
Yevgeniy Karplyuk ◽  
Volodymyr Fesechko
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Wavelet Transform ◽  
Experimental Results ◽  
Dyadic Wavelet ◽  
Heart Rate Fluctuations ◽  
Slow Heart Rate ◽  
Dyadic Wavelet Transform

Estimation of Heart Rate Variability Fluctuations by Wavelet TransformTechnique for separate estimation of fast and slow fluctuations in the heart rate signal is developed. The orthogonal dyadic wavelet transform is used to separate the slow heart rate changes in approximation part of decomposition and fast changes in detail parts. Experimental results using the recordings from persons practicing Chi meditation demonstrated the applicability of estimation heart rate fluctuations with the proposed approach.

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