Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram

Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.

Copy-Move Forgery Detection Using DyWT

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

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.

Molecular and cytogenetic analysis of infertile Hakka men with azoospermia and severe oligozoospermia in southern China

Objective To determine the prevalence of chromosome abnormalities and azoospermia factor (AZF) microdeletions in Hakka men with infertility in southern China. Methods Hakka male patients, who received clinical counselling for infertility between August 2016 and October 2017, and fertile male controls, were enrolled into this retrospective study. Patients diagnosed with infertility and controls underwent cytogenetic analysis by standard G-banding; AZF microdeletions were examined by multiplex polymerase chain reaction and capillary electrophoresis. Results Out of 918 male patients who received fertility counselling, 57 were diagnosed with infertility due to azoospermia or severe oligozoospermia. Of these infertile patients, 22.81% (13/57) carried chromosome abnormalities, with 47, XXY being the most common abnormal karyotype. In addition, 36.84% (21/57) presented with Y chromosome microdeletions, most frequently in the complete AZFc and partial AZFc region. Duplication of the AZFc region was found in three patients. No AZF microdeletions were found in 60 fertile male controls. Conclusion The high AZF microdeletion frequency in the current Hakka population suggests that AZF microdeletion analysis is essential in fertility screening, and combined with cytogenetic analysis, may influence the choice of assisted reproductive techniques and reduce the risk of inherited genetic disease.