Stereolithographic(STL)files have been extensively used in rapid prototyping industries as well as many other fields as watermarking algorithms to secure intellectual property and protect three-dimensional models from...Stereolithographic(STL)files have been extensively used in rapid prototyping industries as well as many other fields as watermarking algorithms to secure intellectual property and protect three-dimensional models from theft.However,to the best of our knowledge,few studies have looked at how watermarking can resist attacks that involve vertex-reordering.Here,we present a lossless and robust watermarking scheme for STL files to protect against vertexreordering attacks.Specifically,we designed a novel error-correcting code(ECC)that can correct the error of any one-bit in a bitstream by inserting several check digits.In addition,ECC is designed to make use of redundant information according to the characteristics of STL files,which introduces further robustness for defense against attacks.No modifications are made to the geometric information of the three-dimensional model,which respects the requirements of a highprecision model.The experimental results show that the proposed watermarking scheme can survive numerous kinds of attack,including rotation,scaling and translation(RST),facet reordering,and vertex-reordering attacks.展开更多
Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques’vulnerability to attack and poor performance in low-quality data.However,difficulties still exi...Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques’vulnerability to attack and poor performance in low-quality data.However,difficulties still exist in how to unify the meaning of heterogeneous scores more effectively.Aside from the matching scores themselves,the importance of the ranking information they include has been undervalued in previous studies.This study concentrates on matching scores and their ranking information and suggests the ranking partition collision(RPC)theory from the standpoint of the worth of scores.To meet both forensic and judicial needs,this paper proposes a method that employs a neural network to fuse biometrics at the score level.In addition,this paper constructs a virtual homologous dataset and conducts experiments on it.Experimental results demonstrate that the proposed method achieves an accuracy of 100%in both mAP and Rank1.To show the efficiency of the proposed method in practical applications,this work carries out more experiments utilizing real-world data.The results show that the proposed approach maintains a Rank1 accuracy of 99.2%on the million-scale database.It offers a novel approach to fusion at the score level.展开更多
基金This work was supported in part by the National Science Foundation of China(No.61772539,6187212,61972405),STITSX(No.201705D131025),1331KITSX,and CiCi3D.
文摘Stereolithographic(STL)files have been extensively used in rapid prototyping industries as well as many other fields as watermarking algorithms to secure intellectual property and protect three-dimensional models from theft.However,to the best of our knowledge,few studies have looked at how watermarking can resist attacks that involve vertex-reordering.Here,we present a lossless and robust watermarking scheme for STL files to protect against vertexreordering attacks.Specifically,we designed a novel error-correcting code(ECC)that can correct the error of any one-bit in a bitstream by inserting several check digits.In addition,ECC is designed to make use of redundant information according to the characteristics of STL files,which introduces further robustness for defense against attacks.No modifications are made to the geometric information of the three-dimensional model,which respects the requirements of a highprecision model.The experimental results show that the proposed watermarking scheme can survive numerous kinds of attack,including rotation,scaling and translation(RST),facet reordering,and vertex-reordering attacks.
基金This work was supported by Double First-Class Innovation Research Project for People’s Public Security University of China(No.2023SYL06).
文摘Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques’vulnerability to attack and poor performance in low-quality data.However,difficulties still exist in how to unify the meaning of heterogeneous scores more effectively.Aside from the matching scores themselves,the importance of the ranking information they include has been undervalued in previous studies.This study concentrates on matching scores and their ranking information and suggests the ranking partition collision(RPC)theory from the standpoint of the worth of scores.To meet both forensic and judicial needs,this paper proposes a method that employs a neural network to fuse biometrics at the score level.In addition,this paper constructs a virtual homologous dataset and conducts experiments on it.Experimental results demonstrate that the proposed method achieves an accuracy of 100%in both mAP and Rank1.To show the efficiency of the proposed method in practical applications,this work carries out more experiments utilizing real-world data.The results show that the proposed approach maintains a Rank1 accuracy of 99.2%on the million-scale database.It offers a novel approach to fusion at the score level.
