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
Purpose – In the process of robot shell design, it is necessary to match the shape of the input 3D originalcharacter mesh model and robot endoskeleton, in order to make the input model fit for robot and avoidcollisio...Purpose – In the process of robot shell design, it is necessary to match the shape of the input 3D originalcharacter mesh model and robot endoskeleton, in order to make the input model fit for robot and avoidcollision. So, the purpose of this paper is to find an object of reference, which can be used for the process ofshape matching.Design/methodology/approach – In this work, the authors propose an interior bounded box (IBB)approach that derives from oriented bounding box (OBB). This kind of box is inside the closed mesh model.At the same time, it has maximum volume which is aligned with the object axis but is enclosed by all the meshvertices. Based on the IBB of input mesh model and the OBB of robot endoskeleton, the authors can completethe process of shape matching. In this paper, the authors use an evolutionary algorithm, covariance matrixadaptation evolution strategy (CMA-ES), to approximate the IBB based on skeleton and symmetry of inputcharacter mesh model.Findings – Based on the evolutionary algorithm CMA-ES, the optimal position and scale informationof IBB can be found. The authors can obtain satisfactory IBB result after this optimization process.The output IBB has maximum volume and is enveloped by the input character mesh model as well.Originality/value – To the best knowledge of the authors, the IBB is first proposed and used in the field ofrobot shell design. Taking advantage of the IBB, people can quickly obtain a shell model that fit for robot.At the same time, it can avoid collision between shell model and the robot endoskeleton.展开更多
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
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This research,which is carried out at BeingThere Centre,collaboration among IMI of Nanyang Technological University(NTU)Singapore,ETH Zurich and UNC Chapel Hill,is supported by the Singapore National Research Foundation(NRF)under its International Research Centre@Singapore Funding Initiative and administered by the Interactive Digital Media Programme Office(IDMPO).The author Shihui Guo is supported by Chinese Post-doctoral Science Foundation 2016M600506.
文摘Purpose – In the process of robot shell design, it is necessary to match the shape of the input 3D originalcharacter mesh model and robot endoskeleton, in order to make the input model fit for robot and avoidcollision. So, the purpose of this paper is to find an object of reference, which can be used for the process ofshape matching.Design/methodology/approach – In this work, the authors propose an interior bounded box (IBB)approach that derives from oriented bounding box (OBB). This kind of box is inside the closed mesh model.At the same time, it has maximum volume which is aligned with the object axis but is enclosed by all the meshvertices. Based on the IBB of input mesh model and the OBB of robot endoskeleton, the authors can completethe process of shape matching. In this paper, the authors use an evolutionary algorithm, covariance matrixadaptation evolution strategy (CMA-ES), to approximate the IBB based on skeleton and symmetry of inputcharacter mesh model.Findings – Based on the evolutionary algorithm CMA-ES, the optimal position and scale informationof IBB can be found. The authors can obtain satisfactory IBB result after this optimization process.The output IBB has maximum volume and is enveloped by the input character mesh model as well.Originality/value – To the best knowledge of the authors, the IBB is first proposed and used in the field ofrobot shell design. Taking advantage of the IBB, people can quickly obtain a shell model that fit for robot.At the same time, it can avoid collision between shell model and the robot endoskeleton.
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.
