Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery d...Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery detection methods are required to identify forged areas.We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern(LTrP)features to detect the single and multiple copy-move attacks from the images.The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations.It also uses discrete wavelet transform(DWT)for dimension reduction.The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods.Finally,Jeffreys and Matusita distance is used for similarity measurement.For the evaluation of the results,three datasets are used,namely MICC-F220,MICC-F2000,and CoMoFoD.Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.展开更多
Recently,automatic diagnosis of diabetic retinopathy(DR)from the retinal image is the most significant ressearch topic in the medical applications.Diabetic macular edema(DME)is the.major reason for the loss of vision ...Recently,automatic diagnosis of diabetic retinopathy(DR)from the retinal image is the most significant ressearch topic in the medical applications.Diabetic macular edema(DME)is the.major reason for the loss of vision in patients suffering fom DR.Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities.Many techniques are.developed to diagnose the DR.The major drawbacks of the existing techniques are low accuracy and high time complexity.To owercome these issues,this paper propases an enhanced particle swarm optimization differential evolution feature selection(PSO DEFS)based feature selection approach with biometric aut hentication for the identification of DR.Initially,a hybrid median filter(HMF)is used for pre processing the input images.Then,the pre-processed images are embedded with each other by using least significant bit(LSB)for authentication purpose.Si-multaneously,the image features are extracted using convoluted local tetra pattern(CLTrP)and Tamura features.Feature selection is performed using PSO DEFS and PSO-gravitational search algorithm(PSO GSA)to reduce time complexity.Based on some performance metrics,the PSO-DEFS is chosen as a better choice for feature selection.The feature selection is performed based on the fitness value.A multi-relevance vector machine(M-RVM)is introduced to dlassify the 13 normal and 62 abnormal images among 75 images from 60 patients.Finally,the DR patients are further dassified by M-RVM.The experimental results exhibit that the proposed approach achieves better accuracy,sensitivity,and specificity than the exist ing techniques.展开更多
文摘Copy-move forgery is the most common type of digital image manipulation,in which the content from the same image is used to forge it.Such manipulations are performed to hide the desired information.Therefore,forgery detection methods are required to identify forged areas.We have introduced a novel method for features computation by employing a circular block-based method through local tetra pattern(LTrP)features to detect the single and multiple copy-move attacks from the images.The proposed method is applied over the circular blocks to efficiently and effectively deal with the post-processing operations.It also uses discrete wavelet transform(DWT)for dimension reduction.The obtained approximate image is distributed into circular blocks on which the LTrP algorithm is employed to calculate the feature vector as the LTrP provides detailed information about the image content by utilizing the direction-based relation of central pixel to its neighborhoods.Finally,Jeffreys and Matusita distance is used for similarity measurement.For the evaluation of the results,three datasets are used,namely MICC-F220,MICC-F2000,and CoMoFoD.Both the qualitative and quantitative analysis shows that the proposed method exhibits state-of-the-art performance under the presence of post-processing operations and can accurately locate single and multiple copy-move forgery attacks on the images.
文摘Recently,automatic diagnosis of diabetic retinopathy(DR)from the retinal image is the most significant ressearch topic in the medical applications.Diabetic macular edema(DME)is the.major reason for the loss of vision in patients suffering fom DR.Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities.Many techniques are.developed to diagnose the DR.The major drawbacks of the existing techniques are low accuracy and high time complexity.To owercome these issues,this paper propases an enhanced particle swarm optimization differential evolution feature selection(PSO DEFS)based feature selection approach with biometric aut hentication for the identification of DR.Initially,a hybrid median filter(HMF)is used for pre processing the input images.Then,the pre-processed images are embedded with each other by using least significant bit(LSB)for authentication purpose.Si-multaneously,the image features are extracted using convoluted local tetra pattern(CLTrP)and Tamura features.Feature selection is performed using PSO DEFS and PSO-gravitational search algorithm(PSO GSA)to reduce time complexity.Based on some performance metrics,the PSO-DEFS is chosen as a better choice for feature selection.The feature selection is performed based on the fitness value.A multi-relevance vector machine(M-RVM)is introduced to dlassify the 13 normal and 62 abnormal images among 75 images from 60 patients.Finally,the DR patients are further dassified by M-RVM.The experimental results exhibit that the proposed approach achieves better accuracy,sensitivity,and specificity than the exist ing techniques.
