Random Grids-Based Threshold Visual Secret Sharing with Improved Visual Quality View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-02-16

AUTHORS

Xuehu Yan , Yuliang Lu , Lintao Liu , Song Wan

ABSTRACT

Visual secret sharing (VSS) by random grids (RG) has gained much attention since it avoids the pixel expansion problem as well as requires no codebook design. However, most of the previous RG-based threshold VSS still suffer from low visual quality or worse reconstructed secrets when more shares are stacked. In this paper, a new RG-based VSS with improved visual quality is proposed. The random bits are utilized to improve the visual quality as well as to decrease the darkness of the reconstructed secret image in the proposed scheme. Experimental results and analyses show the effectiveness of the proposed scheme. More... »

PAGES

209-222

Book

TITLE

Digital Forensics and Watermarking

ISBN

978-3-319-53464-0
978-3-319-53465-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-53465-7_16

DOI

http://dx.doi.org/10.1007/978-3-319-53465-7_16

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1083864176


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Hefei Electronic Engineering Institute, 230037, Hefei, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Hefei Electronic Engineering Institute, 230037, Hefei, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yan", 
        "givenName": "Xuehu", 
        "id": "sg:person.010467364517.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010467364517.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hefei Electronic Engineering Institute, 230037, Hefei, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Hefei Electronic Engineering Institute, 230037, Hefei, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lu", 
        "givenName": "Yuliang", 
        "id": "sg:person.015112370271.93", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015112370271.93"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hefei Electronic Engineering Institute, 230037, Hefei, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Hefei Electronic Engineering Institute, 230037, Hefei, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Lintao", 
        "id": "sg:person.013517427271.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013517427271.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hefei Electronic Engineering Institute, 230037, Hefei, China", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Hefei Electronic Engineering Institute, 230037, Hefei, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wan", 
        "givenName": "Song", 
        "id": "sg:person.012077574322.79", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012077574322.79"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2017-02-16", 
    "datePublishedReg": "2017-02-16", 
    "description": "Visual secret sharing (VSS) by random grids (RG) has gained much attention since it avoids the pixel expansion problem as well as requires no codebook design. However, most of the previous RG-based threshold VSS still suffer from low visual quality or worse reconstructed secrets when more shares are stacked. In this paper, a new RG-based VSS with improved visual quality is proposed. The random bits are utilized to improve the visual quality as well as to decrease the darkness of the reconstructed secret image in the proposed scheme. Experimental results and analyses show the effectiveness of the proposed scheme.", 
    "editor": [
      {
        "familyName": "Shi", 
        "givenName": "Yun Qing", 
        "type": "Person"
      }, 
      {
        "familyName": "Kim", 
        "givenName": "Hyoung Joong", 
        "type": "Person"
      }, 
      {
        "familyName": "Perez-Gonzalez", 
        "givenName": "Fernando", 
        "type": "Person"
      }, 
      {
        "familyName": "Liu", 
        "givenName": "Feng", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-53465-7_16", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-53464-0", 
        "978-3-319-53465-7"
      ], 
      "name": "Digital Forensics and Watermarking", 
      "type": "Book"
    }, 
    "keywords": [
      "visual secret sharing", 
      "threshold visual secret sharing", 
      "random grids", 
      "improved visual quality", 
      "visual quality", 
      "secret sharing", 
      "reconstructed secret image", 
      "pixel expansion problem", 
      "new RG", 
      "low visual quality", 
      "secret image", 
      "codebook design", 
      "random bits", 
      "experimental results", 
      "sharing", 
      "more shares", 
      "expansion problem", 
      "scheme", 
      "grid", 
      "images", 
      "bits", 
      "quality", 
      "secrets", 
      "effectiveness", 
      "design", 
      "attention", 
      "share", 
      "results", 
      "analysis", 
      "darkness", 
      "problem", 
      "paper"
    ], 
    "name": "Random Grids-Based Threshold Visual Secret Sharing with Improved Visual Quality", 
    "pagination": "209-222", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1083864176"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-53465-7_16"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-53465-7_16", 
      "https://app.dimensions.ai/details/publication/pub.1083864176"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-12-01T06:46", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/chapter/chapter_116.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-53465-7_16"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-53465-7_16'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-53465-7_16'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-53465-7_16'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-53465-7_16'


 

This table displays all metadata directly associated to this object as RDF triples.

127 TRIPLES      22 PREDICATES      56 URIs      49 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-53465-7_16 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N2586c3a62a3d45d4b6cdebe0b52fef24
4 schema:datePublished 2017-02-16
5 schema:datePublishedReg 2017-02-16
6 schema:description Visual secret sharing (VSS) by random grids (RG) has gained much attention since it avoids the pixel expansion problem as well as requires no codebook design. However, most of the previous RG-based threshold VSS still suffer from low visual quality or worse reconstructed secrets when more shares are stacked. In this paper, a new RG-based VSS with improved visual quality is proposed. The random bits are utilized to improve the visual quality as well as to decrease the darkness of the reconstructed secret image in the proposed scheme. Experimental results and analyses show the effectiveness of the proposed scheme.
7 schema:editor N7220036057fa42e0b11aba23ed5b98d6
8 schema:genre chapter
9 schema:isAccessibleForFree false
10 schema:isPartOf N2cac25d77643487e952d8bdbaaa4049e
11 schema:keywords analysis
12 attention
13 bits
14 codebook design
15 darkness
16 design
17 effectiveness
18 expansion problem
19 experimental results
20 grid
21 images
22 improved visual quality
23 low visual quality
24 more shares
25 new RG
26 paper
27 pixel expansion problem
28 problem
29 quality
30 random bits
31 random grids
32 reconstructed secret image
33 results
34 scheme
35 secret image
36 secret sharing
37 secrets
38 share
39 sharing
40 threshold visual secret sharing
41 visual quality
42 visual secret sharing
43 schema:name Random Grids-Based Threshold Visual Secret Sharing with Improved Visual Quality
44 schema:pagination 209-222
45 schema:productId N876f88c819f34a4ea80861d6fbbd93f7
46 Nc4f8002c08ea454c843aa597fe22957f
47 schema:publisher N0fc545c21b7b4f3f83757c4e371b1974
48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083864176
49 https://doi.org/10.1007/978-3-319-53465-7_16
50 schema:sdDatePublished 2022-12-01T06:46
51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
52 schema:sdPublisher N90d496d97f4d40989a558632cdf9e66b
53 schema:url https://doi.org/10.1007/978-3-319-53465-7_16
54 sgo:license sg:explorer/license/
55 sgo:sdDataset chapters
56 rdf:type schema:Chapter
57 N0fc545c21b7b4f3f83757c4e371b1974 schema:name Springer Nature
58 rdf:type schema:Organisation
59 N1611c498b4304fae88fd724a9cdbe06e rdf:first N93b55b05aebb4685be51ed14edc2c609
60 rdf:rest Naff0c132b42444febedb247f77f02fc7
61 N165a1b44c82a4c39b7bed503d53f07e5 rdf:first Nff941b05c281426bb7cde408bc76045e
62 rdf:rest N1611c498b4304fae88fd724a9cdbe06e
63 N2586c3a62a3d45d4b6cdebe0b52fef24 rdf:first sg:person.010467364517.31
64 rdf:rest N78e912a19ecb40fda2e6aad9e58b54c3
65 N2cac25d77643487e952d8bdbaaa4049e schema:isbn 978-3-319-53464-0
66 978-3-319-53465-7
67 schema:name Digital Forensics and Watermarking
68 rdf:type schema:Book
69 N2df5ea5cb42a4251b2c0693ad7cab7dc schema:familyName Shi
70 schema:givenName Yun Qing
71 rdf:type schema:Person
72 N5e8289f2106e4089a332fc124f3eeaf5 schema:familyName Liu
73 schema:givenName Feng
74 rdf:type schema:Person
75 N7220036057fa42e0b11aba23ed5b98d6 rdf:first N2df5ea5cb42a4251b2c0693ad7cab7dc
76 rdf:rest N165a1b44c82a4c39b7bed503d53f07e5
77 N78e912a19ecb40fda2e6aad9e58b54c3 rdf:first sg:person.015112370271.93
78 rdf:rest Naa645501ca7a432f81a00c26000d261c
79 N876f88c819f34a4ea80861d6fbbd93f7 schema:name doi
80 schema:value 10.1007/978-3-319-53465-7_16
81 rdf:type schema:PropertyValue
82 N8c1cc02077b74e689395994fd0e98428 rdf:first sg:person.012077574322.79
83 rdf:rest rdf:nil
84 N90d496d97f4d40989a558632cdf9e66b schema:name Springer Nature - SN SciGraph project
85 rdf:type schema:Organization
86 N93b55b05aebb4685be51ed14edc2c609 schema:familyName Perez-Gonzalez
87 schema:givenName Fernando
88 rdf:type schema:Person
89 Naa645501ca7a432f81a00c26000d261c rdf:first sg:person.013517427271.32
90 rdf:rest N8c1cc02077b74e689395994fd0e98428
91 Naff0c132b42444febedb247f77f02fc7 rdf:first N5e8289f2106e4089a332fc124f3eeaf5
92 rdf:rest rdf:nil
93 Nc4f8002c08ea454c843aa597fe22957f schema:name dimensions_id
94 schema:value pub.1083864176
95 rdf:type schema:PropertyValue
96 Nff941b05c281426bb7cde408bc76045e schema:familyName Kim
97 schema:givenName Hyoung Joong
98 rdf:type schema:Person
99 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
100 schema:name Information and Computing Sciences
101 rdf:type schema:DefinedTerm
102 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
103 schema:name Artificial Intelligence and Image Processing
104 rdf:type schema:DefinedTerm
105 sg:person.010467364517.31 schema:affiliation grid-institutes:None
106 schema:familyName Yan
107 schema:givenName Xuehu
108 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010467364517.31
109 rdf:type schema:Person
110 sg:person.012077574322.79 schema:affiliation grid-institutes:None
111 schema:familyName Wan
112 schema:givenName Song
113 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012077574322.79
114 rdf:type schema:Person
115 sg:person.013517427271.32 schema:affiliation grid-institutes:None
116 schema:familyName Liu
117 schema:givenName Lintao
118 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013517427271.32
119 rdf:type schema:Person
120 sg:person.015112370271.93 schema:affiliation grid-institutes:None
121 schema:familyName Lu
122 schema:givenName Yuliang
123 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015112370271.93
124 rdf:type schema:Person
125 grid-institutes:None schema:alternateName Hefei Electronic Engineering Institute, 230037, Hefei, China
126 schema:name Hefei Electronic Engineering Institute, 230037, Hefei, China
127 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...