A novel blind digital watermarking algorithm based on neural networks and multiwavelet transform is presented. The host image is decomposed through multiwavelet transform. There are four subblocks in the LL- level of ...A novel blind digital watermarking algorithm based on neural networks and multiwavelet transform is presented. The host image is decomposed through multiwavelet transform. There are four subblocks in the LL- level of the multiwavelet domain and these subblocks have many similarities. Watermark bits are added to low- frequency coefficients. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results demonstrate that the new algorithm is robust against a variety of attacks, especially, the watermark extraction does not require the original image.展开更多
An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet a...An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system （HVS） characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.展开更多
A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by ad...A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.展开更多
A new image watermarking scheme is proposed to resist rotation, scaling and translation （RST） attacks. Six combined low order image moments are utilized to represent image information on rotation, scaling and transl...A new image watermarking scheme is proposed to resist rotation, scaling and translation （RST） attacks. Six combined low order image moments are utilized to represent image information on rotation, scaling and translation. Affine transform parameters are registered by feedforward neural networks. Watermark is adaptively embedded in discrete wavelet transform （DWT） domain while watermark extraction is carried out without original image after attacked watermarked image has been synchronized by making inverse transform through parameters learned by neural networks. Experimental results show that the proposed scheme can effectively register affine transform parameters, embed watermark more robustly and resist geometric attacks as well as JPEG2000 compression.展开更多
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digita...The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.展开更多
This study uses the digital image correlation technique to measure the crack tip displacement field at various crack lengths in U71MnG rail steel,and the interpolated continuous displacement field was obtained by fitt...This study uses the digital image correlation technique to measure the crack tip displacement field at various crack lengths in U71MnG rail steel,and the interpolated continuous displacement field was obtained by fitting with a back propagation(BP)neural network.The slip and stacking of dislocations affect crack initiation and growth,leading to changes in the crack tip field and the fatigue characteristics of crack growth.The Christopher-James-Patterson(CJP)model describes the elastic stress field around a growing fatigue crack that experiences plasticity-induced shielding.In the present work,this model is modified by including the effect of the dislocation field on the plastic zone of the crack tip and hence on the elastic field by introducing a plastic flow factorρ,which represents the amount of blunting of the crack tip.The Levenberg-Marquardt(L-M)nonlinear least squares method was used to solve for the stress intensity factors.To verify the accuracy of this modified CJP model,the theoretical and experimental plastic zone errors before and after modification were compared,and the variation trends of the stress intensity factors and the plastic flow factorρwere analysed.The results show that the CJP model,with the introduction ofρ,exhibits a good blunting trend.In the low plasticity state,the modified model can accurately describe the experimental plastic zone,and the modified stress intensity factors are more accurate,which proves the effectiveness of dislocation correction.This plastic flow correction provides a more accurate crack tip field model and improves the CJP crack growth relationship.展开更多
A novel adaptive blind image watermarking scheme resistant to Rotation, scaling and translation (RST) attacks is proposed in this paper. Based on fuzzy clustering theory and Human visual system (HVS) model, the spread...A novel adaptive blind image watermarking scheme resistant to Rotation, scaling and translation (RST) attacks is proposed in this paper. Based on fuzzy clustering theory and Human visual system (HVS) model, the spread spectrum watermark is adaptively embedded in Discrete wavelet transform (DWT) domain. In order to register RST transform parameters, a hierarchical neural network is utilized to learn image geometric pattern represented by low order Zernike moments. Watermark extraction is carried out after watermarked image has been synchronized without original image. It only needs a trained neural network.Experiments show that it can embed more robust watermark under certain visual distance, effectively resist Joint photographic experts group (JPEG) compression, noise and RST attacks.展开更多
Deep neural networks(DNNs)are widely used in real-world applications,thanks to their exceptional performance in image recognition.However,their vulnerability to attacks,such as Trojan and data poison,can compromise th...Deep neural networks(DNNs)are widely used in real-world applications,thanks to their exceptional performance in image recognition.However,their vulnerability to attacks,such as Trojan and data poison,can compromise the integrity and stability of DNN applications.Therefore,it is crucial to verify the integrity of DNN models to ensure their security.Previous research on model watermarking for integrity detection has encountered the issue of overexposure of model parameters during embedding and extraction of the watermark.To address this problem,we propose a novel score-based black-box DNN fragile watermarking framework called fragile trigger generation(FTG).The FTG framework only requires the prediction probability distribution of the final output of the classifier during the watermarking process.It generates different fragile samples as the trigger,based on the classification prediction probability of the target classifier and a specified prediction probability mask to watermark it.Different prediction probability masks can promote the generation of fragile samples in corresponding distribution types.The whole watermarking process does not affect the performance of the target classifier.When verifying the watermarking information,the FTG only needs to compare the prediction results of the model on the samples with the previous label.As a result,the required model parameter information is reduced,and the FTG only needs a few samples to detect slight modifications in the model.Experimental results demonstrate the effectiveness of our proposed method and show its superiority over related work.The FTG framework provides a robust solution for verifying the integrity of DNN models,and its effectiveness in detecting slight modifications makes it a valuable tool for ensuring the security and stability of DNN applications.展开更多
基金The National Natural Science Foundation of China(No60473015)
文摘A novel blind digital watermarking algorithm based on neural networks and multiwavelet transform is presented. The host image is decomposed through multiwavelet transform. There are four subblocks in the LL- level of the multiwavelet domain and these subblocks have many similarities. Watermark bits are added to low- frequency coefficients. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results demonstrate that the new algorithm is robust against a variety of attacks, especially, the watermark extraction does not require the original image.
基金Supported by the National Natural Science Foun-dation of China ( 60473015)
文摘An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system （HVS） characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.
文摘A novel lossless information hiding algorithm based on wavelet neural network for digital vector maps is introduced. Wavelet coefficients being manipulated are embedded into a vector map, which could be restored by adjusting the weights of neurons in the designed neural network. When extracting the watermark extraction, those coefficients would be extracted by wavelet decomposition. With the trained multilayer feed forward neural network, the watermark would be obtained finally by measuring the weights of neurons. Experimental results show that the average error coding rate is only 6% for the proposed scheme and compared with other classical algorithms on the same tests, it is indicated that the proposed algorithm hashigher robustness, better invisibility and less loss on precision.
文摘A new image watermarking scheme is proposed to resist rotation, scaling and translation （RST） attacks. Six combined low order image moments are utilized to represent image information on rotation, scaling and translation. Affine transform parameters are registered by feedforward neural networks. Watermark is adaptively embedded in discrete wavelet transform （DWT） domain while watermark extraction is carried out without original image after attacked watermarked image has been synchronized by making inverse transform through parameters learned by neural networks. Experimental results show that the proposed scheme can effectively register affine transform parameters, embed watermark more robustly and resist geometric attacks as well as JPEG2000 compression.
基金Acknowledgements： The work is supported by China＇s National Natural Science Foundation （No. 60573141）, China＇s Project 863 （No. 2004AA775053, No. 2005AA775050）, the High Technique Research Plan of Jiangsu Province （No. BG2005037） and ＂The Six Heights of Talent＂ Program of Jiangsu Province.
基金Project(E2015203354)supported by Natural Science Foundation of Steel United Research Fund of Hebei Province,ChinaProject(ZD2016100)supported by the Science and the Technology Research Key Project of High School of Hebei Province,China+1 种基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,ChinaProject(16LGY015)supported by the Basic Research Special Breeding of Yanshan University,China
文摘The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
基金Supported by Sichuan Science and Technology Program of China (Grant No.2022YFH0075)Opening Project of State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure of China (Grant No.HJGZ2021113)Independent Research Project of State Key Laboratory of Traction Power of China (Grant No.2022TPL_T13)。
文摘This study uses the digital image correlation technique to measure the crack tip displacement field at various crack lengths in U71MnG rail steel,and the interpolated continuous displacement field was obtained by fitting with a back propagation(BP)neural network.The slip and stacking of dislocations affect crack initiation and growth,leading to changes in the crack tip field and the fatigue characteristics of crack growth.The Christopher-James-Patterson(CJP)model describes the elastic stress field around a growing fatigue crack that experiences plasticity-induced shielding.In the present work,this model is modified by including the effect of the dislocation field on the plastic zone of the crack tip and hence on the elastic field by introducing a plastic flow factorρ,which represents the amount of blunting of the crack tip.The Levenberg-Marquardt(L-M)nonlinear least squares method was used to solve for the stress intensity factors.To verify the accuracy of this modified CJP model,the theoretical and experimental plastic zone errors before and after modification were compared,and the variation trends of the stress intensity factors and the plastic flow factorρwere analysed.The results show that the CJP model,with the introduction ofρ,exhibits a good blunting trend.In the low plasticity state,the modified model can accurately describe the experimental plastic zone,and the modified stress intensity factors are more accurate,which proves the effectiveness of dislocation correction.This plastic flow correction provides a more accurate crack tip field model and improves the CJP crack growth relationship.
文摘A novel adaptive blind image watermarking scheme resistant to Rotation, scaling and translation (RST) attacks is proposed in this paper. Based on fuzzy clustering theory and Human visual system (HVS) model, the spread spectrum watermark is adaptively embedded in Discrete wavelet transform (DWT) domain. In order to register RST transform parameters, a hierarchical neural network is utilized to learn image geometric pattern represented by low order Zernike moments. Watermark extraction is carried out after watermarked image has been synchronized without original image. It only needs a trained neural network.Experiments show that it can embed more robust watermark under certain visual distance, effectively resist Joint photographic experts group (JPEG) compression, noise and RST attacks.
基金supported by Research Funders National Natural Science Foundation of China(62172001,U22B2047,62076147).
文摘Deep neural networks(DNNs)are widely used in real-world applications,thanks to their exceptional performance in image recognition.However,their vulnerability to attacks,such as Trojan and data poison,can compromise the integrity and stability of DNN applications.Therefore,it is crucial to verify the integrity of DNN models to ensure their security.Previous research on model watermarking for integrity detection has encountered the issue of overexposure of model parameters during embedding and extraction of the watermark.To address this problem,we propose a novel score-based black-box DNN fragile watermarking framework called fragile trigger generation(FTG).The FTG framework only requires the prediction probability distribution of the final output of the classifier during the watermarking process.It generates different fragile samples as the trigger,based on the classification prediction probability of the target classifier and a specified prediction probability mask to watermark it.Different prediction probability masks can promote the generation of fragile samples in corresponding distribution types.The whole watermarking process does not affect the performance of the target classifier.When verifying the watermarking information,the FTG only needs to compare the prediction results of the model on the samples with the previous label.As a result,the required model parameter information is reduced,and the FTG only needs a few samples to detect slight modifications in the model.Experimental results demonstrate the effectiveness of our proposed method and show its superiority over related work.The FTG framework provides a robust solution for verifying the integrity of DNN models,and its effectiveness in detecting slight modifications makes it a valuable tool for ensuring the security and stability of DNN applications.
