Meaningful visual secret sharing based on error diffusion and random grids View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-01-09

AUTHORS

Shen Wang, Xuehu Yan, Jianzhi Sang, Xiamu Niu

ABSTRACT

Random grid (RG) is an efficient method of eliminating the drawback of pixel expansion problem in visual secret sharing (VSS). Error diffusion (ED) technique is a brilliant method that improves the diffusion performance in an image by reducing the pattern noise and removing boundary and ’blackhole’ effects. In this paper, a novel meaningful RG-ED-based VSS, which encodes the (k, n) threshold into meaningful shadow images, is proposed at the price of not-clear recovered images. In addition, the novel scheme realizes the (k, n) threshold, avoids the design of complex codebook and averts the pixel expansion problem. Furthermore, the proposed RG-ED-based VSS inherits conventional benefits of VSS without the need of cryptographic efforts to decode the secret. Compared with other schemes reported in the literature, the present scheme has the benefits mentioned above, at the price of possible degrading of recovered images’ quality. More... »

PAGES

3353-3373

References to SciGraph publications

  • 2011-07-16. (2,n) secret sharing scheme for gray and color images based on Boolean operation in SCIENCE CHINA INFORMATION SCIENCES
  • 2010. A Comprehensive Study of Visual Cryptography in TRANSACTIONS ON DATA HIDING AND MULTIMEDIA SECURITY V
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11042-014-2438-8

    DOI

    http://dx.doi.org/10.1007/s11042-014-2438-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1011716809


    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": "School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China", 
              "id": "http://www.grid.ac/institutes/grid.19373.3f", 
              "name": [
                "School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wang", 
            "givenName": "Shen", 
            "id": "sg:person.013117766471.90", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013117766471.90"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China", 
              "id": "http://www.grid.ac/institutes/grid.19373.3f", 
              "name": [
                "School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 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": "School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China", 
              "id": "http://www.grid.ac/institutes/grid.19373.3f", 
              "name": [
                "School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Sang", 
            "givenName": "Jianzhi", 
            "id": "sg:person.015342734145.75", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015342734145.75"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China", 
              "id": "http://www.grid.ac/institutes/grid.19373.3f", 
              "name": [
                "School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Niu", 
            "givenName": "Xiamu", 
            "id": "sg:person.011071034626.54", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011071034626.54"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/978-3-642-14298-7_5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050017989", 
              "https://doi.org/10.1007/978-3-642-14298-7_5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11432-011-4302-z", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013818124", 
              "https://doi.org/10.1007/s11432-011-4302-z"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2015-01-09", 
        "datePublishedReg": "2015-01-09", 
        "description": "Random grid (RG) is an efficient method of eliminating the drawback of pixel expansion problem in visual secret sharing (VSS). Error diffusion (ED) technique is a brilliant method that improves the diffusion performance in an image by reducing the pattern noise and removing boundary and \u2019blackhole\u2019 effects. In this paper, a novel meaningful RG-ED-based VSS, which encodes the (k, n) threshold into meaningful shadow images, is proposed at the price of not-clear recovered images. In addition, the novel scheme realizes the (k, n) threshold, avoids the design of complex codebook and averts the pixel expansion problem. Furthermore, the proposed RG-ED-based VSS inherits conventional benefits of VSS without the need of cryptographic efforts to decode the secret. Compared with other schemes reported in the literature, the present scheme has the benefits mentioned above, at the price of possible degrading of recovered images\u2019 quality.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11042-014-2438-8", 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1044869", 
            "issn": [
              "1380-7501", 
              "1573-7721"
            ], 
            "name": "Multimedia Tools and Applications", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "6", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "75"
          }
        ], 
        "keywords": [
          "visual secret sharing", 
          "pixel expansion problem", 
          "random grids", 
          "secret sharing", 
          "meaningful shadow images", 
          "error diffusion technique", 
          "error diffusion", 
          "shadow images", 
          "complex codebooks", 
          "expansion problem", 
          "novel scheme", 
          "pattern noise", 
          "image quality", 
          "sharing", 
          "conventional benefits", 
          "images", 
          "scheme", 
          "brilliant method", 
          "grid", 
          "efficient method", 
          "blackhole", 
          "codebook", 
          "secrets", 
          "diffusion performance", 
          "drawbacks", 
          "quality", 
          "method", 
          "performance", 
          "noise", 
          "benefits", 
          "present scheme", 
          "design", 
          "technique", 
          "need", 
          "efforts", 
          "threshold", 
          "prices", 
          "diffusion technique", 
          "degrading", 
          "literature", 
          "addition", 
          "diffusion", 
          "problem", 
          "effect", 
          "paper"
        ], 
        "name": "Meaningful visual secret sharing based on error diffusion and random grids", 
        "pagination": "3353-3373", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1011716809"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11042-014-2438-8"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11042-014-2438-8", 
          "https://app.dimensions.ai/details/publication/pub.1011716809"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-12-01T06:32", 
        "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/article/article_648.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11042-014-2438-8"
      }
    ]
     

    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/s11042-014-2438-8'

    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/s11042-014-2438-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11042-014-2438-8'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11042-014-2438-8'


     

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

    131 TRIPLES      21 PREDICATES      71 URIs      61 LITERALS      6 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11042-014-2438-8 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N6eda1842a995466287e563beb9d47921
    4 schema:citation sg:pub.10.1007/978-3-642-14298-7_5
    5 sg:pub.10.1007/s11432-011-4302-z
    6 schema:datePublished 2015-01-09
    7 schema:datePublishedReg 2015-01-09
    8 schema:description Random grid (RG) is an efficient method of eliminating the drawback of pixel expansion problem in visual secret sharing (VSS). Error diffusion (ED) technique is a brilliant method that improves the diffusion performance in an image by reducing the pattern noise and removing boundary and ’blackhole’ effects. In this paper, a novel meaningful RG-ED-based VSS, which encodes the (k, n) threshold into meaningful shadow images, is proposed at the price of not-clear recovered images. In addition, the novel scheme realizes the (k, n) threshold, avoids the design of complex codebook and averts the pixel expansion problem. Furthermore, the proposed RG-ED-based VSS inherits conventional benefits of VSS without the need of cryptographic efforts to decode the secret. Compared with other schemes reported in the literature, the present scheme has the benefits mentioned above, at the price of possible degrading of recovered images’ quality.
    9 schema:genre article
    10 schema:isAccessibleForFree false
    11 schema:isPartOf N11a2b08ff59b4c139e24e7461d487430
    12 N35eac47ad67742169787314fd9375091
    13 sg:journal.1044869
    14 schema:keywords addition
    15 benefits
    16 blackhole
    17 brilliant method
    18 codebook
    19 complex codebooks
    20 conventional benefits
    21 degrading
    22 design
    23 diffusion
    24 diffusion performance
    25 diffusion technique
    26 drawbacks
    27 effect
    28 efficient method
    29 efforts
    30 error diffusion
    31 error diffusion technique
    32 expansion problem
    33 grid
    34 image quality
    35 images
    36 literature
    37 meaningful shadow images
    38 method
    39 need
    40 noise
    41 novel scheme
    42 paper
    43 pattern noise
    44 performance
    45 pixel expansion problem
    46 present scheme
    47 prices
    48 problem
    49 quality
    50 random grids
    51 scheme
    52 secret sharing
    53 secrets
    54 shadow images
    55 sharing
    56 technique
    57 threshold
    58 visual secret sharing
    59 schema:name Meaningful visual secret sharing based on error diffusion and random grids
    60 schema:pagination 3353-3373
    61 schema:productId N7fb9488354f84b239566248c74592937
    62 Nd4a64333315b453e90e22169b7b2c902
    63 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011716809
    64 https://doi.org/10.1007/s11042-014-2438-8
    65 schema:sdDatePublished 2022-12-01T06:32
    66 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    67 schema:sdPublisher N851ea70af44d4336819649e43c88f5e7
    68 schema:url https://doi.org/10.1007/s11042-014-2438-8
    69 sgo:license sg:explorer/license/
    70 sgo:sdDataset articles
    71 rdf:type schema:ScholarlyArticle
    72 N11a2b08ff59b4c139e24e7461d487430 schema:issueNumber 6
    73 rdf:type schema:PublicationIssue
    74 N1e857e72489143c3abe8abf6ad2a4dd5 rdf:first sg:person.015342734145.75
    75 rdf:rest N680a573f75314a67afedea4dbab11dfd
    76 N35eac47ad67742169787314fd9375091 schema:volumeNumber 75
    77 rdf:type schema:PublicationVolume
    78 N680a573f75314a67afedea4dbab11dfd rdf:first sg:person.011071034626.54
    79 rdf:rest rdf:nil
    80 N6eda1842a995466287e563beb9d47921 rdf:first sg:person.013117766471.90
    81 rdf:rest Nd7973f57d4614a7d941091eb176800ea
    82 N7fb9488354f84b239566248c74592937 schema:name doi
    83 schema:value 10.1007/s11042-014-2438-8
    84 rdf:type schema:PropertyValue
    85 N851ea70af44d4336819649e43c88f5e7 schema:name Springer Nature - SN SciGraph project
    86 rdf:type schema:Organization
    87 Nd4a64333315b453e90e22169b7b2c902 schema:name dimensions_id
    88 schema:value pub.1011716809
    89 rdf:type schema:PropertyValue
    90 Nd7973f57d4614a7d941091eb176800ea rdf:first sg:person.010467364517.31
    91 rdf:rest N1e857e72489143c3abe8abf6ad2a4dd5
    92 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    93 schema:name Information and Computing Sciences
    94 rdf:type schema:DefinedTerm
    95 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    96 schema:name Artificial Intelligence and Image Processing
    97 rdf:type schema:DefinedTerm
    98 sg:journal.1044869 schema:issn 1380-7501
    99 1573-7721
    100 schema:name Multimedia Tools and Applications
    101 schema:publisher Springer Nature
    102 rdf:type schema:Periodical
    103 sg:person.010467364517.31 schema:affiliation grid-institutes:grid.19373.3f
    104 schema:familyName Yan
    105 schema:givenName Xuehu
    106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010467364517.31
    107 rdf:type schema:Person
    108 sg:person.011071034626.54 schema:affiliation grid-institutes:grid.19373.3f
    109 schema:familyName Niu
    110 schema:givenName Xiamu
    111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011071034626.54
    112 rdf:type schema:Person
    113 sg:person.013117766471.90 schema:affiliation grid-institutes:grid.19373.3f
    114 schema:familyName Wang
    115 schema:givenName Shen
    116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013117766471.90
    117 rdf:type schema:Person
    118 sg:person.015342734145.75 schema:affiliation grid-institutes:grid.19373.3f
    119 schema:familyName Sang
    120 schema:givenName Jianzhi
    121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015342734145.75
    122 rdf:type schema:Person
    123 sg:pub.10.1007/978-3-642-14298-7_5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050017989
    124 https://doi.org/10.1007/978-3-642-14298-7_5
    125 rdf:type schema:CreativeWork
    126 sg:pub.10.1007/s11432-011-4302-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1013818124
    127 https://doi.org/10.1007/s11432-011-4302-z
    128 rdf:type schema:CreativeWork
    129 grid-institutes:grid.19373.3f schema:alternateName School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
    130 schema:name School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
    131 rdf:type schema:Organization
     




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


    ...