With the continuous advancement of steganographic techniques,the task of image steganalysis has become increasingly challenging,posing significant obstacles to the fields of information security and digital forensics....With the continuous advancement of steganographic techniques,the task of image steganalysis has become increasingly challenging,posing significant obstacles to the fields of information security and digital forensics.Although existing deep learning methods have achieved certain progress in steganography detection,they still encounter several difficulties in real-world applications.Specifically,current methods often struggle to accurately focus on steganography sensitive regions,leading to limited detection accuracy.Moreover,feature information is frequently lost during transmission,which further reduces the model’s generalization ability.These issues not only compromise the reliability of steganography detection but also hinder its applicability in complex scenarios.To address these challenges,this paper proposes a novel deep image steganalysis network designed to enhance detection accuracy and improve the retention of steganographic information through multilevel feature optimization and global perceptual modeling.The network consists of three core modules:the preprocessing module,the feature extraction module,and the classification module.In the preprocessing stage,a Spatial Rich Model(SRM)filter is introduced to extract the high-frequency residual information of the image to initially enhance the steganographic features;at the same time,a lightweight Densely Connected Convolutional Networks(DenseNet)structure is proposed to enhance the effective transmission and retention of the features and alleviate the information loss problem in the deep network.In the feature extraction stage,a hybrid modeling structure combining depth-separated convolution and ordinary convolution is constructed to improve the feature extraction efficiency and feature description capability;in addition,a dual-domain adaptive attention mechanism integrating channel and spatial dimensions is designed to dynamically allocate feature weights to achieve precise focusing on the steganography-sensitive region.Finally,the classification module adopts dual fully connected layers to realize the effective differentiation between coverage and steganography maps.These innovative designs not only effectively improve the accuracy and generalization ability of steganography detection,but also provide a new efficient network structure for the field of steganalysis.Numerous experimental results show that the detection performance of the proposed method outperforms the existing mainstream methods,such as SR-Net,TSNet,and CVTStego-Net,on the publicly available dataset BOSSbase and BOSW2.Meanwhile,multiple ablation experiments further validate the validity and reasonableness of the proposed network structure.These results not only promote the development of steganalysis technology but also provide more reliable detection tools for the fields of information security and digital forensics.展开更多
In oil and gas exploration,small-scale karst cavities and faults are important targets.The former often serve as reservoir space for carbonate reservoirs,while the latter often provide migration pathways for oil and g...In oil and gas exploration,small-scale karst cavities and faults are important targets.The former often serve as reservoir space for carbonate reservoirs,while the latter often provide migration pathways for oil and gas.Due to these differences,the classification and identification of karst cavities and faults are of great significance for reservoir development.Traditional seismic attributes and diffraction imaging techniques can effectively identify discontinuities in seismic images,but these techniques do not distinguish whether these discontinuities are karst cavities,faults,or other structures.It poses a challenge for seismic interpretation to accurately locate and classify karst cavities or faults within the seismic attribute maps and diffraction imaging profiles.In seismic data,the scattering waves are associated with small-scale scatters like karst cavities,while diffracted waves are seismic responses from discontinuous structures such as faults,reflector edges and fractures.In order to achieve classification and identification of small-scale karst cavities and faults in seismic images,we propose a diffraction classification imaging method which classifies diffracted and scattered waves in the azimuth-dip angle image matrix using a modified DenseNet.We introduce a coordinate attention module into DenseNet,enabling more precise extraction of dynamic and azimuthal features of diffracted and scattered waves in the azimuth-dip angle image matrix.Leveraging these extracted features,the modified DenseNet can produce reliable probabilities for diffracted/scattered waves,achieving high-accuracy automatic classification of cavities and faults based on diffraction imaging.The proposed method achieves 96%classification accuracy on the synthetic dataset.The field data experiment demonstrates that the proposed method can accurately classify small-scale faults and scatterers,further enhancing the resolution of diffraction imaging in complex geologic structures,and contributing to the localization of karstic fracture-cavern reservoirs.展开更多
基金supported in part by Gansu Province Higher Education Institutions Industrial Support Program under Grant 2020C 29in part by the National Natural Science Foundation of China under Grant 61562002.
文摘With the continuous advancement of steganographic techniques,the task of image steganalysis has become increasingly challenging,posing significant obstacles to the fields of information security and digital forensics.Although existing deep learning methods have achieved certain progress in steganography detection,they still encounter several difficulties in real-world applications.Specifically,current methods often struggle to accurately focus on steganography sensitive regions,leading to limited detection accuracy.Moreover,feature information is frequently lost during transmission,which further reduces the model’s generalization ability.These issues not only compromise the reliability of steganography detection but also hinder its applicability in complex scenarios.To address these challenges,this paper proposes a novel deep image steganalysis network designed to enhance detection accuracy and improve the retention of steganographic information through multilevel feature optimization and global perceptual modeling.The network consists of three core modules:the preprocessing module,the feature extraction module,and the classification module.In the preprocessing stage,a Spatial Rich Model(SRM)filter is introduced to extract the high-frequency residual information of the image to initially enhance the steganographic features;at the same time,a lightweight Densely Connected Convolutional Networks(DenseNet)structure is proposed to enhance the effective transmission and retention of the features and alleviate the information loss problem in the deep network.In the feature extraction stage,a hybrid modeling structure combining depth-separated convolution and ordinary convolution is constructed to improve the feature extraction efficiency and feature description capability;in addition,a dual-domain adaptive attention mechanism integrating channel and spatial dimensions is designed to dynamically allocate feature weights to achieve precise focusing on the steganography-sensitive region.Finally,the classification module adopts dual fully connected layers to realize the effective differentiation between coverage and steganography maps.These innovative designs not only effectively improve the accuracy and generalization ability of steganography detection,but also provide a new efficient network structure for the field of steganalysis.Numerous experimental results show that the detection performance of the proposed method outperforms the existing mainstream methods,such as SR-Net,TSNet,and CVTStego-Net,on the publicly available dataset BOSSbase and BOSW2.Meanwhile,multiple ablation experiments further validate the validity and reasonableness of the proposed network structure.These results not only promote the development of steganalysis technology but also provide more reliable detection tools for the fields of information security and digital forensics.
基金supported by Science Fund for Creative Research Groups of the National Natural Science Foundation of China,No.42321002。
文摘In oil and gas exploration,small-scale karst cavities and faults are important targets.The former often serve as reservoir space for carbonate reservoirs,while the latter often provide migration pathways for oil and gas.Due to these differences,the classification and identification of karst cavities and faults are of great significance for reservoir development.Traditional seismic attributes and diffraction imaging techniques can effectively identify discontinuities in seismic images,but these techniques do not distinguish whether these discontinuities are karst cavities,faults,or other structures.It poses a challenge for seismic interpretation to accurately locate and classify karst cavities or faults within the seismic attribute maps and diffraction imaging profiles.In seismic data,the scattering waves are associated with small-scale scatters like karst cavities,while diffracted waves are seismic responses from discontinuous structures such as faults,reflector edges and fractures.In order to achieve classification and identification of small-scale karst cavities and faults in seismic images,we propose a diffraction classification imaging method which classifies diffracted and scattered waves in the azimuth-dip angle image matrix using a modified DenseNet.We introduce a coordinate attention module into DenseNet,enabling more precise extraction of dynamic and azimuthal features of diffracted and scattered waves in the azimuth-dip angle image matrix.Leveraging these extracted features,the modified DenseNet can produce reliable probabilities for diffracted/scattered waves,achieving high-accuracy automatic classification of cavities and faults based on diffraction imaging.The proposed method achieves 96%classification accuracy on the synthetic dataset.The field data experiment demonstrates that the proposed method can accurately classify small-scale faults and scatterers,further enhancing the resolution of diffraction imaging in complex geologic structures,and contributing to the localization of karstic fracture-cavern reservoirs.
