Dear Editor,This letter deals with the distributed recursive set-membership filtering(DRSMF)issue for state-saturated systems under encryption-decryption mechanism.To guarantee the data security,the encryption-decrypt...Dear Editor,This letter deals with the distributed recursive set-membership filtering(DRSMF)issue for state-saturated systems under encryption-decryption mechanism.To guarantee the data security,the encryption-decryption mechanism is considered in the signal transmission process.Specifically,a novel DRSMF scheme is developed such that,for both state saturation and encryption-decryption mechanism,the filtering error(FE)is limited to the ellipsoid domain.Then,the filtering error constraint matrix(FECM)is computed and a desirable filter gain is derived by minimizing the FECM.Besides,the bound-edness evaluation of the FECM is provided.展开更多
Three-dimensional (3D) point cloud information hiding algorithms are mainly concentrated in the spatialdomain. Existing spatial domain steganalysis algorithms are subject to more disturbing factors during the analysis...Three-dimensional (3D) point cloud information hiding algorithms are mainly concentrated in the spatialdomain. Existing spatial domain steganalysis algorithms are subject to more disturbing factors during the analysisand detection process, and can only be applied to 3D mesh objects, so there is a lack of steganalysis algorithms for 3Dpoint cloud objects. To change the fact that steganalysis is limited to 3D mesh and eliminate the redundant featuresin the 3D mesh steganalysis feature set, we propose a 3D point cloud steganalysis algorithm based on compositeoperator feature enhancement. First, the 3D point cloud is normalized and smoothed. Second, the feature pointsthat may contain secret information in 3D point clouds and their neighboring points are extracted as the featureenhancement region by the improved 3DHarris-ISS composite operator. Feature enhancement is performed in thefeature enhancement region to form a feature-enhanced 3D point cloud, which highlights the feature points whilesuppressing the interference created by the rest of the vertices. Third, the existing 3D mesh feature set is screenedto reduce the data redundancy of more relevant features, and the newly proposed local neighborhood feature setis added to the screened feature set to form the 3D point cloud steganography feature set POINT72. Finally,the steganographic features are extracted from the enhanced 3D point cloud using the POINT72 feature set, andsteganalysis experiments are carried out. Experimental analysis shows that the algorithm can accurately analyzethe 3D point cloud’s spatial steganography and determine whether the 3D point cloud contains hidden information,so the accuracy of 3D point cloud steganalysis, under the prerequisite of missing edge and face information, is closeto that of the existing 3D mesh steganalysis algorithms.展开更多
基金supported by the National Natural Science Foundation of China(12471416,12171124,12301567)the Heilongjiang Provincial Natural Science Foundation of China(PL2024F015)+2 种基金the Postdoctoral Science Foundation of Heilongjiang Province of China(LBH-Z22199)the Fundamental Research Foun-dation for Universities of Heilongjiang Province of China(2022-KYYWF-0141)the Alexander von Humboldt Foundation of Germany.
文摘Dear Editor,This letter deals with the distributed recursive set-membership filtering(DRSMF)issue for state-saturated systems under encryption-decryption mechanism.To guarantee the data security,the encryption-decryption mechanism is considered in the signal transmission process.Specifically,a novel DRSMF scheme is developed such that,for both state saturation and encryption-decryption mechanism,the filtering error(FE)is limited to the ellipsoid domain.Then,the filtering error constraint matrix(FECM)is computed and a desirable filter gain is derived by minimizing the FECM.Besides,the bound-edness evaluation of the FECM is provided.
基金supported by the National Natural Science Foundation of China(No.62372062)。
文摘Three-dimensional (3D) point cloud information hiding algorithms are mainly concentrated in the spatialdomain. Existing spatial domain steganalysis algorithms are subject to more disturbing factors during the analysisand detection process, and can only be applied to 3D mesh objects, so there is a lack of steganalysis algorithms for 3Dpoint cloud objects. To change the fact that steganalysis is limited to 3D mesh and eliminate the redundant featuresin the 3D mesh steganalysis feature set, we propose a 3D point cloud steganalysis algorithm based on compositeoperator feature enhancement. First, the 3D point cloud is normalized and smoothed. Second, the feature pointsthat may contain secret information in 3D point clouds and their neighboring points are extracted as the featureenhancement region by the improved 3DHarris-ISS composite operator. Feature enhancement is performed in thefeature enhancement region to form a feature-enhanced 3D point cloud, which highlights the feature points whilesuppressing the interference created by the rest of the vertices. Third, the existing 3D mesh feature set is screenedto reduce the data redundancy of more relevant features, and the newly proposed local neighborhood feature setis added to the screened feature set to form the 3D point cloud steganography feature set POINT72. Finally,the steganographic features are extracted from the enhanced 3D point cloud using the POINT72 feature set, andsteganalysis experiments are carried out. Experimental analysis shows that the algorithm can accurately analyzethe 3D point cloud’s spatial steganography and determine whether the 3D point cloud contains hidden information,so the accuracy of 3D point cloud steganalysis, under the prerequisite of missing edge and face information, is closeto that of the existing 3D mesh steganalysis algorithms.
