When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model ...When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.展开更多
Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be ef...Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.展开更多
Nowadays,digital images can be easily tampered due to the availability of powerful image processing software.As digital cameras continue to replace their analog counterparts,the importance of authenticating digital im...Nowadays,digital images can be easily tampered due to the availability of powerful image processing software.As digital cameras continue to replace their analog counterparts,the importance of authenticating digital images,identifying their sources,and detecting forgeries is increasing.Blind image forensics is used to analyze an image in the complete absence of any digital watermark or signature.Image compositing is the most common form of digital tampering.Assuming that image compositing operations affect the inherent statistics of the image,we propose an image compositing detection method on based on a statistical model for natural image in the wavelet transform domain.The generalized Gaussian model(CGD)is employed to describe the marginal distribution of wavelet coefficients of images,and the parameters of GGD are obtained using maximumlikelihood estimator.The statistical features include GGD parameters,prediction error,mean,variance,skewness,and kurtosis at each wavelet detail subband.Then,these feature vectors are used to discriminate between natural images and composite images using support vector machine(SVM).To evaluate the performance of our proposed method,we carried out tests on the Columbia Uncompressed Image Splicing Detection Dataset and another advanced dataset,and achieved a detection accuracy of 92%and 79%,respectively.The detection performance of our method is better than that of the method using camera response function on the same dataset.展开更多
文摘When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.
文摘Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.
文摘Nowadays,digital images can be easily tampered due to the availability of powerful image processing software.As digital cameras continue to replace their analog counterparts,the importance of authenticating digital images,identifying their sources,and detecting forgeries is increasing.Blind image forensics is used to analyze an image in the complete absence of any digital watermark or signature.Image compositing is the most common form of digital tampering.Assuming that image compositing operations affect the inherent statistics of the image,we propose an image compositing detection method on based on a statistical model for natural image in the wavelet transform domain.The generalized Gaussian model(CGD)is employed to describe the marginal distribution of wavelet coefficients of images,and the parameters of GGD are obtained using maximumlikelihood estimator.The statistical features include GGD parameters,prediction error,mean,variance,skewness,and kurtosis at each wavelet detail subband.Then,these feature vectors are used to discriminate between natural images and composite images using support vector machine(SVM).To evaluate the performance of our proposed method,we carried out tests on the Columbia Uncompressed Image Splicing Detection Dataset and another advanced dataset,and achieved a detection accuracy of 92%and 79%,respectively.The detection performance of our method is better than that of the method using camera response function on the same dataset.
