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1.发明申请公开(公告)号：US20190251268A1
公开(公告)日：2019-08-15
申请号：US15905121
申请日：2018-02-26
发明人： Sukhwan Lee , Eungju Lee , Dong Yeop Lee , Ju Hyeon Jeong
摘要： Disclosed is a reversible DNA information hiding method based on prediction-error expansion and histogram shifting, the method being capable of false start codon prevention, original sequence length preservation, high watermark capacity, and blind detection based on prediction-error expansion and histogram shifting without biological mutation.
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2.发明申请公开(公告)号：US20190207753A1
公开(公告)日：2019-07-04
申请号：US15905276
申请日：2018-02-26
发明人： Sukhwan Lee , Deokjin Song , Jaewan Cho
摘要： The present invention relates to a method of hashing a perceptual vector model based on a multi-scale curvature. According to a first aspect, there is provided a method of hashing a perceptual vector model based on a multi-scale curvature including: generating a multi-dimensional feature coefficient matrix, and obtaining a multi-dimensional intermediate hash coefficient matrix; and obtaining a final binary hash matrix, and enabling the multi-dimensional binary hash matrix to be hierarchically authenticated. In addition, according to a second aspect, there is provided a method of hashing a perceptual vector model based on a multi-scale curvature including: generating a hash by using a hash function; and authenticating a vector model. In addition, an error detection probability for an object attack can be lower by about 2×10−5˜2.8×10−2, and a uniqueness probability is raised by about 0.014. In addition, an entropy can be raised by about 0.875˜2.149.
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3.发明授权公开(公告)号：US11115190B2
公开(公告)日：2021-09-07
申请号：US15905276
申请日：2018-02-26
发明人： Sukhwan Lee , Eung Joo Lee
摘要： The present invention relates to a method of hashing a perceptual vector model based on a multi-scale curvature. According to a first aspect, there is provided a method of hashing a perceptual vector model based on a multi-scale curvature including: generating a multi-dimensional feature coefficient matrix, and obtaining a multi-dimensional intermediate hash coefficient matrix; and obtaining a final binary hash matrix, and enabling the multi-dimensional binary hash matrix to be hierarchically authenticated. In addition, according to a second aspect, there is provided a method of hashing a perceptual vector model based on a multi-scale curvature including: generating a hash by using a hash function; and authenticating a vector model. In addition, an error detection probability for an object attack can be lower by about 2×10−5˜2.8×10−2, and a uniqueness probability is raised by about 0.014. In addition, an entropy can be raised by about 0.875˜2.149.
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