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.

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.

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.

A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valli Bhasha A. ◽  
Venkatramana Reddy B.D.
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Discrete Wavelet ◽  
Content Type ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Better Than

Purpose The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem. Design/methodology/approach This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models. Findings From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images. Originality/value This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

Multi-Focus Image Fusion Using Local Energy Pattern

2011 ◽  
Vol 145 ◽  
pp. 119-123
Author(s):  
Ko Chin Chang
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Local Energy ◽  
View Point ◽  
Fusion Algorithm ◽  
Image Capture ◽  
Focus Image ◽  
Better Than

For general image capture device, it is difficult to obtain an image with every object in focus. To solve the fusion issue of multiple same view point images with different focal settings, a novel image fusion algorithm based on local energy pattern (LGP) is proposed in this paper. Firstly, each focus images is decomposed using discrete wavelet transform (DWT) separately. Secondly, to calculate LGP with the corresponding pixel and its surrounding pixels, then use LGP to compute the new coefficient of the pixel from each transformed images with our proposed weighted fusing rules. The rules use different operations in low-bands coefficients and high-bands coefficients. Finally, the generated image is reconstructed from the new subband coefficients. Moreover, the reconstructed image can represent more detailed for the obtained scene. Experimental results demonstrate that our scheme performs better than the traditional discrete cosine transform (DCT) and discrete wavelet transform (DWT) method in both visual perception and quantitative analysis.

Solving Time Complexity Issue in Copy-Move Forgery Detection Thru Pre-processing Techniques

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Siti Fadzlun Md Salleh ◽  
Mohd Foad Rohani ◽  
Mohd Aizaini Maarof Maarof
Keyword(s):  
Time Complexity ◽  
Processing Technique ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Complexity Issue ◽  
Processing Step ◽  
Copy Move Forgery Detection ◽  
Processing Techniques

Copy-move forgery detection (CMFD) has become a popular an important research focus in digital image forensic. Copy-move forgery happens when a region in an image is copied and paste into the same image. Apart from the main problem of detection robustness and accuracy, CMFD is struggle with time complexity issue. One of the options to resolve this problem was by including pre-processing step in CMFD pipeline. This paper reviews on the importance of pre-processing step, and available techniques in reducing time complexity of copy-move forgery detection. An experiment using discrete wavelet transform (DWT) as a pre-processing technique was carried out to evaluate the performance of adopting pre-processing technique in CMFD pipeline. The experimental result has shown a significant reduction in processing time with some trade off to detection accuracy.

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