Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the sa...Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion);geometry attacks (letter box and rotation);and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.展开更多
Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. There...Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.展开更多
AIM： To determine the diagnostic yield of the “third eye retroscope”, on adenoma detection rate during screening colonoscopy.METHODS： The “third eye retroscope” when used with standard colonoscopy provides an ad...AIM： To determine the diagnostic yield of the “third eye retroscope”, on adenoma detection rate during screening colonoscopy.METHODS： The “third eye retroscope” when used with standard colonoscopy provides an additional retro-grade view to visualize lesions on the proximal aspects of folds and fexures. We searched MEDLINE （PubMed and Ovid）, SCOPUS （including MEDLINE and EMBASE databases）, Cochrane Database of Systemic Reviews, Google Scholar, and CINAHL Plus databases to identify studies that evaluated diagnostic yield of “third eye retroscope” during screening colonoscopy. DerSimonian Laird random effects model was used to generate the overall effect for each outcome. We evaluated statistical heterogeneity among the studies by using the Cochran Q statistic and quantifed by I2 statistics.RESULTS： Four distinct studies with a total of 920 pa-tients, mean age 59.83 （95%CI： 56.77-62.83） years, were included in the review. The additional adenoma detection rate （AADR） defined as the number of ad-ditional adenomas identified due to “third eye retro-scope” device in comparison to standard colonoscopy alone was 19.9% （95%CI： 7.3-43.9）. AADR for right and left colon were 13.9% （95%CI： 9.4-20） and 10.7 （95%CI： 1.9-42）, respectively. AADR for polyps ≥ 6 mm and ≥ 10 mm were 24.6% （95%CI： 16.6-34.9） and 24.2% （95%CI： 12.9-40.8）, respectively. The ad-ditional polyp detection rate defined as the number of additional polyps identifed due to “third eye retro-scope” device in comparison to standard colonoscopyalone was 19.8% （95%CI： 7.9-41.8）. There were no complications reported with use of “third eye retro-scope” device.CONCLUSION： The “third eye retroscope” device when used with standard colonoscopy is safe and de-tects 19.9% additional adenomas, compared to stan-dard colonoscopy alone.展开更多
The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content.An effective technique for tampering the identification is the copy-mo...The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content.An effective technique for tampering the identification is the copy-move forgery.Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification.Contrast-ingly,deep learning(DL)models have demonstrated significant performance over the other statistical techniques.With this motivation,this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection(ODTL-CMFD)technique.The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered,and then localize the copy moved regions.To perform the feature extraction process,the political optimizer(PO)with Mobile Networks(MobileNet)model has been derived for generating a set of useful vectors.Finally,an enhanced bird swarm algorithm(EBSA)with least square support vector machine(LS-SVM)model has been employed for classifying the digital images into the original or the forged ones.The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine(MSVM)technique and thereby enhance the classification performance.For ensuring the enhanced performance of the ODTL-CMFD technique,a series of simulations have been performed against the benchmark MICC-F220,MICC-F2000,and MICC-F600 datasets.The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.展开更多
Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm ...Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform （SIFT）-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region.展开更多
Objective:To summarize the application value of copy number variant sequencing(CNV-seq)in the detection of fetal chromosome and cytomegalovirus load.Methods:The study analyzed the clinical basic data,relevant laborato...Objective:To summarize the application value of copy number variant sequencing(CNV-seq)in the detection of fetal chromosome and cytomegalovirus load.Methods:The study analyzed the clinical basic data,relevant laboratory tests,treatment process,and outcomes of three patients with positive cytomegalovirus load detected by CNV-seq for fetal chromosomes and cytomegalovirus load,and literature review was done simutaneoubly.Results:In all three cases,the amniotic fluid cytomegalovirus load was less than 105 Copies/ml,and there were no significant neurological abnormalities observed during pregnancy or postpartum follow-up.There is no literature review on the application of CNV-seq technology in the detection of cytomegalovirus infection,only literature reports on genome analysis of CMV-DNA in confirmed patients were available.Conclusion:CNV-seq can be used to detect cytomegalovirus load,which may have a certain degree of predictive value for fetal outcome.CNV-seq can simultaneously detect fetal chromosomes and pathogenic microorganisms,which is of great significance for the prevention and control of birth defects.展开更多
文摘Content-based copy detection (CBCD) is widely used in copyright control for protecting unauthorized use of digital video and its key issue is to extract robust fingerprint against different attacked versions of the same video. In this paper, the “natural parts” (coarse scales) of the Shearlet coefficients are used to generate robust video fingerprints for content-based video copy detection applications. The proposed Shearlet-based video fingerprint (SBVF) is constructed by the Shearlet coefficients in Scale 1 (lowest coarse scale) for revealing the spatial features and Scale 2 (second lowest coarse scale) for revealing the directional features. To achieve spatiotemporal natural, the proposed SBVF is applied to Temporal Informative Representative Image (TIRI) of the video sequences for final fingerprints generation. A TIRI-SBVF based CBCD system is constructed with use of Invert Index File (IIF) hash searching approach for performance evaluation and comparison using TRECVID 2010 dataset. Common attacks are imposed in the queries such as luminance attacks (luminance change, salt and pepper noise, Gaussian noise, text insertion);geometry attacks (letter box and rotation);and temporal attacks (dropping frame, time shifting). The experimental results demonstrate that the proposed TIRI-SBVF fingerprinting algorithm is robust on CBCD applications on most of the attacks. It can achieve an average F1 score of about 0.99, less than 0.01% of false positive rate (FPR) and 97% accuracy of localization.
文摘Copy-move offense is considerably used to conceal or hide several data in the digital image for specific aim, and onto this offense some portion of the genuine image is reduplicated and pasted in the same image. Therefore, Copy-Move forgery is a very significant problem and active research area to check the confirmation of the image. In this paper, a system for Copy Move Forgery detection is proposed. The proposed system is composed of two stages: one is called the detection stages and the second is called the refine detection stage. The detection stage is executed using Speeded-Up Robust Feature (SURF) and Binary Robust Invariant Scalable Keypoints (BRISK) for feature detection and in the refine detection stage, image registration using non-linear transformation is used to enhance detection efficiency. Initially, the genuine image is picked, and then both SURF and BRISK feature extractions are used in parallel to detect the interest keypoints. This gives an appropriate number of interest points and gives the assurance for finding the majority of the manipulated regions. RANSAC is employed to find the superior group of matches to differentiate the manipulated parts. Then, non-linear transformation between the best-matched sets from both extraction features is used as an optimization to get the best-matched set and detect the copied regions. A number of numerical experiments performed using many benchmark datasets such as, the CASIA v2.0, MICC-220, MICC-F600 and MICC-F2000 datasets. With the proposed algorithm, an overall average detection accuracy of 95.33% is obtained for evaluation carried out with the aforementioned databases. Forgery detection achieved True Positive Rate of 97.4% for tampered images with object translation, different degree of rotation and enlargement. Thus, results from different datasets have been set, proving that the proposed algorithm can individuate the altered areas, with high reliability and dealing with multiple cloning.
文摘AIM： To determine the diagnostic yield of the “third eye retroscope”, on adenoma detection rate during screening colonoscopy.METHODS： The “third eye retroscope” when used with standard colonoscopy provides an additional retro-grade view to visualize lesions on the proximal aspects of folds and fexures. We searched MEDLINE （PubMed and Ovid）, SCOPUS （including MEDLINE and EMBASE databases）, Cochrane Database of Systemic Reviews, Google Scholar, and CINAHL Plus databases to identify studies that evaluated diagnostic yield of “third eye retroscope” during screening colonoscopy. DerSimonian Laird random effects model was used to generate the overall effect for each outcome. We evaluated statistical heterogeneity among the studies by using the Cochran Q statistic and quantifed by I2 statistics.RESULTS： Four distinct studies with a total of 920 pa-tients, mean age 59.83 （95%CI： 56.77-62.83） years, were included in the review. The additional adenoma detection rate （AADR） defined as the number of ad-ditional adenomas identified due to “third eye retro-scope” device in comparison to standard colonoscopy alone was 19.9% （95%CI： 7.3-43.9）. AADR for right and left colon were 13.9% （95%CI： 9.4-20） and 10.7 （95%CI： 1.9-42）, respectively. AADR for polyps ≥ 6 mm and ≥ 10 mm were 24.6% （95%CI： 16.6-34.9） and 24.2% （95%CI： 12.9-40.8）, respectively. The ad-ditional polyp detection rate defined as the number of additional polyps identifed due to “third eye retro-scope” device in comparison to standard colonoscopyalone was 19.8% （95%CI： 7.9-41.8）. There were no complications reported with use of “third eye retro-scope” device.CONCLUSION： The “third eye retroscope” device when used with standard colonoscopy is safe and de-tects 19.9% additional adenomas, compared to stan-dard colonoscopy alone.
文摘The extensive availability of advanced digital image technologies and image editing tools has simplified the way of manipulating the image content.An effective technique for tampering the identification is the copy-move forgery.Conventional image processing techniques generally search for the patterns linked to the fake content and restrict the usage in massive data classification.Contrast-ingly,deep learning(DL)models have demonstrated significant performance over the other statistical techniques.With this motivation,this paper presents an Optimal Deep Transfer Learning based Copy Move Forgery Detection(ODTL-CMFD)technique.The presented ODTL-CMFD technique aims to derive a DL model for the classification of target images into the original and the forged/tampered,and then localize the copy moved regions.To perform the feature extraction process,the political optimizer(PO)with Mobile Networks(MobileNet)model has been derived for generating a set of useful vectors.Finally,an enhanced bird swarm algorithm(EBSA)with least square support vector machine(LS-SVM)model has been employed for classifying the digital images into the original or the forged ones.The utilization of the EBSA algorithm helps to properly modify the parameters contained in the Multiclass Support Vector Machine(MSVM)technique and thereby enhance the classification performance.For ensuring the enhanced performance of the ODTL-CMFD technique,a series of simulations have been performed against the benchmark MICC-F220,MICC-F2000,and MICC-F600 datasets.The experimental results have demonstrated the improvised performance of the ODTL-CMFD approach over the other techniques in terms of several evaluation measures.
基金Supported by the National Natural Science Foundation of China(61472429,61070192,91018008,61303074,61170240)the National High Technology Research Development Program of China(863 Program)(2007AA01Z414)+1 种基金the National Science and Technology Major Project of China(2012ZX01039-004)the Beijing Natural Science Foundation(4122041)
文摘Small or smooth cloned regions are difficult to be detected in image copy-move forgery （CMF） detection. Aiming at this problem, an effective method based on image segmentation and swarm intelligent （SI） algorithm is proposed. This method segments image into small nonoverlapping blocks. A calculation of smooth degree is given for each block. Test image is segmented into independent layers according to the smooth degree. SI algorithm is applied in finding the optimal detection parameters for each layer. These parameters are used to detect each layer by scale invariant features transform （SIFT）-based scheme, which can locate a mass of keypoints. The experimental results prove the good performance of the proposed method, which is effective to identify the CMF image with small or smooth cloned region.
基金Hainan Natural Science Foundation(821RC699)Hainan Natural Science Foundation(822RC825)+1 种基金Hainan Provincial Health Industry Research Project(22A200242)Key R&D Plan of Hainan Province(ZDYF2020225)。
文摘Objective:To summarize the application value of copy number variant sequencing(CNV-seq)in the detection of fetal chromosome and cytomegalovirus load.Methods:The study analyzed the clinical basic data,relevant laboratory tests,treatment process,and outcomes of three patients with positive cytomegalovirus load detected by CNV-seq for fetal chromosomes and cytomegalovirus load,and literature review was done simutaneoubly.Results:In all three cases,the amniotic fluid cytomegalovirus load was less than 105 Copies/ml,and there were no significant neurological abnormalities observed during pregnancy or postpartum follow-up.There is no literature review on the application of CNV-seq technology in the detection of cytomegalovirus infection,only literature reports on genome analysis of CMV-DNA in confirmed patients were available.Conclusion:CNV-seq can be used to detect cytomegalovirus load,which may have a certain degree of predictive value for fetal outcome.CNV-seq can simultaneously detect fetal chromosomes and pathogenic microorganisms,which is of great significance for the prevention and control of birth defects.
