Reversible data hiding in encrypted domain(RDH-ED)fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility.Current research on RDH-ED focuses on 2D images,while re...Reversible data hiding in encrypted domain(RDH-ED)fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility.Current research on RDH-ED focuses on 2D images,while research on 3D mesh models is still immature.This paper introduces an RDH-ED method using block modulus encryption and multi-MSB prediction.Initially,the original mesh model is partitioned into non-overlapping subblocks of equal size,and then the vertices in each subblock are encrypted with the same key for additive mod-ulus encryption,ensuring that the spatial correlation present in the original mesh subblocks remains preserved within the encrypted subblocks.Subsequently,the subblocks are disrupted one by one using the 3D Arnold Transform to enhance security.The vertices in each embeddable subblock are divided into a reference vertex and several embeddable vertices,where the multi-MSB prediction strategy is employed to allocate embedding room for each embeddable vertex.Finally,the secret information is embedded into the allocated room.Since the proposed method almost completely preserves the spatial correlation within each subblock,the achieved embedding rate surpasses that of all previous outstanding methods that rely on vacating room after encryption(VRAE).The experimentalfindings demonstrate that the proposed approach achieves an average embedding rate of 45.55 bits per vertex(bpv),surpassing the state-of-the-art method that achieves 25.63 bpv.展开更多
This paper describes a novel algorithm for fragile watermarking of 3D models. Fragile watermarking requires detection of even minute intentional changes to the 3D model along with the location of the change. This pose...This paper describes a novel algorithm for fragile watermarking of 3D models. Fragile watermarking requires detection of even minute intentional changes to the 3D model along with the location of the change. This poses a challenge since inserting random amount of watermark in all the vertices of the model would generally introduce perceptible distortion. The proposed algorithm overcomes this challenge by using genetic algorithm to modify every vertex location in the model so that there is no perceptible distortion. Various experimental results are used to justify the choice of the genetic algorithm design parameters. Experimental results also indicate that the proposed algorithm can accurately detect location of any mesh modification.展开更多
The use of support vector machines （SVM） for watermarking of 3D mesh models is investigated. SVMs have been widely explored for images, audio, and video watermarking but to date the potential of SVMs has not been ex...The use of support vector machines （SVM） for watermarking of 3D mesh models is investigated. SVMs have been widely explored for images, audio, and video watermarking but to date the potential of SVMs has not been explored in the 3D watermarking domain. The proposed approach utilizes SVM as a binary classifier for the selection of vertices for watermark embedding. The SVM is trained with feature vectors derived from the angular difference between the eigen normal and surface normals of a 1-ring neighborhood of vertices taken from normalized 3D mesh models. The SVM learns to classify vertices as appropriate or inappropriate candidates for modification in order to accommodate the watermark. Experimental results verify that the proposed algorithm is imperceptible and robust against attacks such as mesh smoothing, cropping and noise addition.展开更多
We investigated how density and quality of mesh around interest domain affect electromagnetic （EM） responses of 3D Earth layered media using finite element method （FEM）. Effect of different mesh shapes was also in...We investigated how density and quality of mesh around interest domain affect electromagnetic （EM） responses of 3D Earth layered media using finite element method （FEM）. Effect of different mesh shapes was also investigated using a method of mixing structured and unstructured mesh. As a case study, we estimated the effects of meshing on selectivity phenomenon of seismic electric signal （SES）. Our results suggest that the relative errors resulting from mesh effects may not be negligible, which may lead to some unconvincing explanation of the SES selectivity based on the numerical modeling results.展开更多
加密域可逆数据隐藏(reversible data hiding in encrypted domain,RDHED)技术可在保护载体隐私的同时嵌入秘密信息,但当前针对3D网格模型的RDHED方法普遍面临嵌入容量低的难题.针对这一问题,提出了一种基于八叉树分块和顶点划分策略的...加密域可逆数据隐藏(reversible data hiding in encrypted domain,RDHED)技术可在保护载体隐私的同时嵌入秘密信息,但当前针对3D网格模型的RDHED方法普遍面临嵌入容量低的难题.针对这一问题,提出了一种基于八叉树分块和顶点划分策略的加密3D网格模型可逆数据隐藏方法.首先,采用八叉树结构将模型自适应地划分为不重叠子块,保留块内空间相关性;其次,设计基于顶点熵的划分策略,精确选取参考顶点以提升预测精度;最后,采用自适应MSB(most significant bit)预测方法,最大化每个顶点的可嵌入空间,从而显著提升嵌入容量.实验结果表明,该方法在提高3D网格模型嵌入容量的同时,确保了数据的可逆性与可分离性,为3D模型的可逆数据隐藏提供了一种有效的解决方案.展开更多
传统的基于网格的统计形状模型在医学图像分析中得到广泛应用,但在处理复杂的血管几何形态,如血管的分叉、弯曲和斑块变形时仍精度不足。提出了一种基于点云表征的统计形状模型,通过动态图卷积神经网络与空间注意力机制的融合算法直接...传统的基于网格的统计形状模型在医学图像分析中得到广泛应用,但在处理复杂的血管几何形态,如血管的分叉、弯曲和斑块变形时仍精度不足。提出了一种基于点云表征的统计形状模型,通过动态图卷积神经网络与空间注意力机制的融合算法直接处理离散三维坐标点云数据,从而建立了无拓扑约束的血管形态统计模型。实验采用114例颈动脉TOF-MRA影像数据集,经混合滤波预处理后构建点云与网格双模型对比体系。结果显示:点云模型在网格质量的均匀性上提升42%(Jacobi系数变异系数从0.13变为0.08),更适用于后续的流体力学仿真分析。此外,在保留90%形态变异的前提下,点云模型的主成分维度较传统网格模型降低18%(9 vs 11),并且在后续的特异性及泛化性评估上点云模型都展示出更强的鲁棒性。展开更多
基金supported by the National Natural Science Foundation of China(62172001)the Provincial Colleges Quality Project of Anhui Province(2020xsxxkc047)the National Undergraduate Innovation and Entrepreneurship Training Program(2023103570289).
文摘Reversible data hiding in encrypted domain(RDH-ED)fortifies data security and privacy safeguards while upholding the original data’s integrity and accessibility.Current research on RDH-ED focuses on 2D images,while research on 3D mesh models is still immature.This paper introduces an RDH-ED method using block modulus encryption and multi-MSB prediction.Initially,the original mesh model is partitioned into non-overlapping subblocks of equal size,and then the vertices in each subblock are encrypted with the same key for additive mod-ulus encryption,ensuring that the spatial correlation present in the original mesh subblocks remains preserved within the encrypted subblocks.Subsequently,the subblocks are disrupted one by one using the 3D Arnold Transform to enhance security.The vertices in each embeddable subblock are divided into a reference vertex and several embeddable vertices,where the multi-MSB prediction strategy is employed to allocate embedding room for each embeddable vertex.Finally,the secret information is embedded into the allocated room.Since the proposed method almost completely preserves the spatial correlation within each subblock,the achieved embedding rate surpasses that of all previous outstanding methods that rely on vacating room after encryption(VRAE).The experimentalfindings demonstrate that the proposed approach achieves an average embedding rate of 45.55 bits per vertex(bpv),surpassing the state-of-the-art method that achieves 25.63 bpv.
文摘This paper describes a novel algorithm for fragile watermarking of 3D models. Fragile watermarking requires detection of even minute intentional changes to the 3D model along with the location of the change. This poses a challenge since inserting random amount of watermark in all the vertices of the model would generally introduce perceptible distortion. The proposed algorithm overcomes this challenge by using genetic algorithm to modify every vertex location in the model so that there is no perceptible distortion. Various experimental results are used to justify the choice of the genetic algorithm design parameters. Experimental results also indicate that the proposed algorithm can accurately detect location of any mesh modification.
文摘The use of support vector machines （SVM） for watermarking of 3D mesh models is investigated. SVMs have been widely explored for images, audio, and video watermarking but to date the potential of SVMs has not been explored in the 3D watermarking domain. The proposed approach utilizes SVM as a binary classifier for the selection of vertices for watermark embedding. The SVM is trained with feature vectors derived from the angular difference between the eigen normal and surface normals of a 1-ring neighborhood of vertices taken from normalized 3D mesh models. The SVM learns to classify vertices as appropriate or inappropriate candidates for modification in order to accommodate the watermark. Experimental results verify that the proposed algorithm is imperceptible and robust against attacks such as mesh smoothing, cropping and noise addition.
基金partially supported by the National R & D Special Fund of Public Welfare Industry(No.200808069)National Natural Science Foundation of China(Nos.40974038 and 41025014)the Joint Research Collaboration Program by the Ministry of Science and Technology of China(No.2010DFA21570)
文摘We investigated how density and quality of mesh around interest domain affect electromagnetic （EM） responses of 3D Earth layered media using finite element method （FEM）. Effect of different mesh shapes was also investigated using a method of mixing structured and unstructured mesh. As a case study, we estimated the effects of meshing on selectivity phenomenon of seismic electric signal （SES）. Our results suggest that the relative errors resulting from mesh effects may not be negligible, which may lead to some unconvincing explanation of the SES selectivity based on the numerical modeling results.
文摘加密域可逆数据隐藏(reversible data hiding in encrypted domain,RDHED)技术可在保护载体隐私的同时嵌入秘密信息,但当前针对3D网格模型的RDHED方法普遍面临嵌入容量低的难题.针对这一问题,提出了一种基于八叉树分块和顶点划分策略的加密3D网格模型可逆数据隐藏方法.首先,采用八叉树结构将模型自适应地划分为不重叠子块,保留块内空间相关性;其次,设计基于顶点熵的划分策略,精确选取参考顶点以提升预测精度;最后,采用自适应MSB(most significant bit)预测方法,最大化每个顶点的可嵌入空间,从而显著提升嵌入容量.实验结果表明,该方法在提高3D网格模型嵌入容量的同时,确保了数据的可逆性与可分离性,为3D模型的可逆数据隐藏提供了一种有效的解决方案.
文摘传统的基于网格的统计形状模型在医学图像分析中得到广泛应用,但在处理复杂的血管几何形态,如血管的分叉、弯曲和斑块变形时仍精度不足。提出了一种基于点云表征的统计形状模型,通过动态图卷积神经网络与空间注意力机制的融合算法直接处理离散三维坐标点云数据,从而建立了无拓扑约束的血管形态统计模型。实验采用114例颈动脉TOF-MRA影像数据集,经混合滤波预处理后构建点云与网格双模型对比体系。结果显示:点云模型在网格质量的均匀性上提升42%(Jacobi系数变异系数从0.13变为0.08),更适用于后续的流体力学仿真分析。此外,在保留90%形态变异的前提下,点云模型的主成分维度较传统网格模型降低18%(9 vs 11),并且在后续的特异性及泛化性评估上点云模型都展示出更强的鲁棒性。
