AGIM-net based subject-sensitive hashing algorithm for integrity authentication of HRRS images

The premise of effective use of high-resolution remote sensing (HRRS) images is that the data integrity and authenticity of HRRS images must be guaranteed. This paper proposes a new subject-sensitive hashing algorithm for the integrity authentication of HRRS images. This algorithm takes AGIM-net (At...

Full description

Saved in:
Bibliographic Details
Published inGeocarto international Vol. 38; no. 1
Main Authors Ding, Kaimeng, Zeng, Yue, Wang, Yingying, Lv, Dong, Yan, Xinyun
Format Journal Article
LanguageEnglish
Published Taylor & Francis 31.12.2023
Taylor & Francis Group
Subjects
algorithms
attention gate
Attention mechanism
exhibitions
extracts
gates
image analysis
integrity authentication
perceptual hashing
principal component analysis
remote sensing
subject-sensitive hashing
tampering
U-net
Online AccessGet full text

Cover

Abstract The premise of effective use of high-resolution remote sensing (HRRS) images is that the data integrity and authenticity of HRRS images must be guaranteed. This paper proposes a new subject-sensitive hashing algorithm for the integrity authentication of HRRS images. This algorithm takes AGIM-net (Attention Gate-based improved M-net) proposed in this paper to extract the subject-sensitive features of the HRRS images, and uses Principal Component Analysis (PCA) based method to compress and encode the extracted features. AGIM-net is an improved U-net based on attention mechanism, adding multi-scale input in the encoder stage to extract rich image features; adding multi-scale output in the decoder stage, and suppressing the features irrelevant to the subject through Attention Gate to improve the robustness of the algorithm. Experiments show that the proposed algorithm has improved robustness compared with existing algorithms, and the tamper sensitivity and security are basically equivalent to the existing algorithms.
AbstractList The premise of effective use of high-resolution remote sensing (HRRS) images is that the data integrity and authenticity of HRRS images must be guaranteed. This paper proposes a new subject-sensitive hashing algorithm for the integrity authentication of HRRS images. This algorithm takes AGIM-net (Attention Gate-based improved M-net) proposed in this paper to extract the subject-sensitive features of the HRRS images, and uses Principal Component Analysis (PCA) based method to compress and encode the extracted features. AGIM-net is an improved U-net based on attention mechanism, adding multi-scale input in the encoder stage to extract rich image features; adding multi-scale output in the decoder stage, and suppressing the features irrelevant to the subject through Attention Gate to improve the robustness of the algorithm. Experiments show that the proposed algorithm has improved robustness compared with existing algorithms, and the tamper sensitivity and security are basically equivalent to the existing algorithms.
Author Yan, Xinyun
Wang, Yingying
Ding, Kaimeng
Zeng, Yue
Lv, Dong
Author_xml – sequence: 1
  givenname: Kaimeng
  surname: Ding
  fullname: Ding, Kaimeng
  organization: Jiangsu AI Transportation Innovations & Applications Engineering Research Center
– sequence: 2
  givenname: Yue
  surname: Zeng
  fullname: Zeng, Yue
  organization: Jinling Institute of Technology
– sequence: 3
  givenname: Yingying
  surname: Wang
  fullname: Wang, Yingying
  organization: Jinling Institute of Technology
– sequence: 4
  givenname: Dong
  surname: Lv
  fullname: Lv, Dong
  organization: HeBei Branch of China National Geological Exploration Center of Building Material Industry
– sequence: 5
  givenname: Xinyun
  surname: Yan
  fullname: Yan, Xinyun
  organization: Jiangsu AI Transportation Innovations & Applications Engineering Research Center
BookMark eNqFkUtvEzEUhUeoSLSFn4DkJZsJfo09IzZUFbSRWiEV2GLdeK4njiZ2sZ2i_Hucpt10ASs_dM7x8f3OmpMQAzbNe0YXjPb0I6OMKiqHBadcLDhTPdXsVXPKdMdbqhU_qfuqaQ-iN81ZzhtKhe6VOG1-XVwtb9uAhawg40jybrVBW9qMIfviH5CsIa99mAjMU0y-rLfExUR8KDjV457ArqwxFG-h-BhIdOT67u478VuYML9tXjuYM757Ws-bn1-__Li8bm--XS0vL25aKztVWuiFHmnPHOsld6OwAIpbicB7kNBpBhq4FR0Ow6gQpRol9oePjorTfpDivFkec8cIG3Of6utpbyJ483gR02Qg1Y4zGroaV1zqETVoCYoOIB0wwa1znbCdq1kfjln3Kf7eYS5m67PFeYaAcZeNoLJOT8tBVOmno9SmmHNCZ6wvj3MoCfxsGDUHQuaZkDkQMk-Eqrt74X4u_j_f56PPh4piC39imkdTYD_H5BIE62vJf0f8BXAQqaE
CitedBy_id crossref_primary_10_3390_w15193511
crossref_primary_10_3390_rs15194860
crossref_primary_10_3390_ijgi13090336
Cites_doi 10.1109/JIOT.2020.2995148
10.1109/TNNLS.2019.2957276
10.1109/CVPR.2018.00418
10.1016/S0030-4018(03)01261-6
10.1007/s11042-017-4809-4
10.3390/ijgi9080485
10.1016/j.knosys.2021.107807
10.3390/app9152972
10.1016/j.sigpro.2013.01.008
10.1049/iet-ipr.2013.0332
10.1007/978-3-319-24574-4_28
10.1007/s00034-018-0822-8
10.1109/TMI.2019.2903562
10.1016/j.jvcir.2012.03.005
10.1186/s13640-017-0179-0
10.1016/j.image.2019.115713
10.1016/j.dsp.2012.11.002
10.1109/TGRS.2022.3231215
10.1007/s11042-012-1188-8
10.1109/IGARSS47720.2021.9553872
10.1007/s11042-015-2851-7
10.1109/TCYB.2019.2914351
10.1007/s11042-018-5799-6
10.1109/ICMEW.2017.8026309
10.1186/s40064-016-3639-6
10.1109/TPAMI.2011.103
10.3390/ijgi9040254
10.1109/TGRS.2020.2994057
10.1109/TPAMI.2012.48
10.1109/TMI.2020.3010102
10.3390/rs13245109
10.1109/JSTSP.2019.2955012
10.1109/TGRS.2019.2907932
10.1016/j.neunet.2019.08.025
10.1088/1755-1315/440/3/032138
10.1109/TMI.2018.2791488
10.1109/ICSIDP47821.2019.9173237
10.1109/DDCLS49620.2020.9275068
ContentType Journal Article
Copyright 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2023
Copyright_xml – notice: 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2023
DBID 0YH
AAYXX
CITATION
7S9
L.6
DOA
DOI 10.1080/10106049.2023.2168071
DatabaseName Taylor & Francis Open Access
CrossRef
AGRICOLA
AGRICOLA - Academic
DOAJ Open Access Full Text
DatabaseTitle CrossRef
AGRICOLA
AGRICOLA - Academic
DatabaseTitleList
AGRICOLA
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: 0YH
  name: Taylor & Francis Open Access
  url: https://www.tandfonline.com
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Geography
Physics
EISSN 1752-0762
ExternalDocumentID oai_doaj_org_article_0bdb247de7a74a609a4fa132cff53c5f
10_1080_10106049_2023_2168071
2168071
Genre Research Article
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: 41801303
GroupedDBID .7F
.QJ
0BK
0YH
30N
4.4
5GY
5VS
AAENE
AAHBH
AAJMT
ABCCY
ABFIM
ABHAV
ABJNI
ABPAQ
ABPEM
ABTAI
ABXYU
ACGFS
ACTIO
ADCVX
ADGTB
AEISY
AENEX
AEOZL
AEPSL
AEYOC
AFKVX
AGDLA
AGMYJ
AHDZW
AIJEM
AJWEG
AKBVH
AKOOK
ALMA_UNASSIGNED_HOLDINGS
ALQZU
AQRUH
AVBZW
BLEHA
CCCUG
CE4
CS3
DGEBU
DKSSO
EBS
E~A
E~B
F5P
GROUPED_DOAJ
GTTXZ
H13
HF~
HZ~
H~P
IPNFZ
J.P
KYCEM
LJTGL
M4Z
NA5
O9-
P2P
RIG
ROSJB
S-T
SJN
SNACF
TDBHL
TFL
TFT
TFW
TNC
TQWBC
TTHFI
TUROJ
TWF
UPT
UT5
UU3
~02
~S~
AAYXX
AFRVT
AIYEW
CITATION
NX~
7S9
L.6
ID FETCH-LOGICAL-c456t-a837d081f1842fd3caa62c4ea28a4a571a7a2c35e99d6ee46d4e82168d6208943
IEDL.DBID 0YH
ISSN 1010-6049
1752-0762
IngestDate Wed Aug 27 01:29:51 EDT 2025
Wed Jul 02 04:32:39 EDT 2025
Thu Apr 24 22:52:34 EDT 2025
Tue Jul 01 01:58:20 EDT 2025
Wed Dec 25 09:04:08 EST 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Language English
License open-access: http://creativecommons.org/licenses/by/4.0/: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c456t-a837d081f1842fd3caa62c4ea28a4a571a7a2c35e99d6ee46d4e82168d6208943
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
OpenAccessLink https://www.tandfonline.com/doi/abs/10.1080/10106049.2023.2168071
PQID 3040377493
PQPubID 24069
ParticipantIDs proquest_miscellaneous_3040377493
doaj_primary_oai_doaj_org_article_0bdb247de7a74a609a4fa132cff53c5f
crossref_primary_10_1080_10106049_2023_2168071
crossref_citationtrail_10_1080_10106049_2023_2168071
informaworld_taylorfrancis_310_1080_10106049_2023_2168071
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2023-12-31
PublicationDateYYYYMMDD 2023-12-31
PublicationDate_xml – month: 12
  year: 2023
  text: 2023-12-31
  day: 31
PublicationDecade 2020
PublicationTitle Geocarto international
PublicationYear 2023
Publisher Taylor & Francis
Taylor & Francis Group
Publisher_xml – name: Taylor & Francis
– name: Taylor & Francis Group
References Ji SP (e_1_3_3_17_1) 2019; 48
e_1_3_3_30_1
Adiga VS (e_1_3_3_2_1) 2019
Oktay O (e_1_3_3_27_1) 2018
Lin H (e_1_3_3_22_1) 2021; 47
e_1_3_3_18_1
e_1_3_3_39_1
e_1_3_3_19_1
Alom MZ (e_1_3_3_3_1) 2019; 6
e_1_3_3_14_1
e_1_3_3_37_1
e_1_3_3_13_1
e_1_3_3_38_1
e_1_3_3_16_1
e_1_3_3_35_1
e_1_3_3_15_1
Song W (e_1_3_3_32_1) 2022; 60
e_1_3_3_36_1
e_1_3_3_10_1
e_1_3_3_33_1
e_1_3_3_34_1
e_1_3_3_12_1
e_1_3_3_31_1
e_1_3_3_11_1
e_1_3_3_40_1
Niu XM (e_1_3_3_26_1) 2008; 36
e_1_3_3_41_1
Lin TY (e_1_3_3_21_1) 2017; 42
e_1_3_3_7_1
e_1_3_3_6_1
e_1_3_3_9_1
e_1_3_3_8_1
e_1_3_3_29_1
e_1_3_3_28_1
e_1_3_3_25_1
e_1_3_3_24_1
e_1_3_3_46_1
e_1_3_3_47_1
e_1_3_3_44_1
e_1_3_3_20_1
e_1_3_3_45_1
e_1_3_3_5_1
e_1_3_3_23_1
e_1_3_3_42_1
e_1_3_3_4_1
e_1_3_3_43_1
References_xml – volume: 6
  start-page: 014006
  issue: 1
  year: 2019
  ident: e_1_3_3_3_1
  article-title: Recurrent residual U-Net for medical image segmentation
  publication-title: J Med Imaging (Bellingham)
– ident: e_1_3_3_16_1
  doi: 10.1109/JIOT.2020.2995148
– ident: e_1_3_3_42_1
  doi: 10.1109/TNNLS.2019.2957276
– ident: e_1_3_3_40_1
  doi: 10.1109/CVPR.2018.00418
– volume: 60
  start-page: 1
  year: 2022
  ident: e_1_3_3_32_1
  article-title: Asymmetric hash code learning for remote sensing image retrieval
  publication-title: IEEE Trans Geosci Remote Sens
– ident: e_1_3_3_31_1
  doi: 10.1016/S0030-4018(03)01261-6
– ident: e_1_3_3_28_1
  doi: 10.1007/s11042-017-4809-4
– ident: e_1_3_3_6_1
  doi: 10.3390/ijgi9080485
– ident: e_1_3_3_35_1
  doi: 10.1016/j.knosys.2021.107807
– ident: e_1_3_3_7_1
  doi: 10.3390/app9152972
– ident: e_1_3_3_37_1
  doi: 10.1016/j.sigpro.2013.01.008
– ident: e_1_3_3_12_1
  doi: 10.1049/iet-ipr.2013.0332
– ident: e_1_3_3_30_1
  doi: 10.1007/978-3-319-24574-4_28
– ident: e_1_3_3_11_1
  doi: 10.1007/s00034-018-0822-8
– ident: e_1_3_3_10_1
  doi: 10.1109/TMI.2019.2903562
– ident: e_1_3_3_39_1
  doi: 10.1016/j.jvcir.2012.03.005
– ident: e_1_3_3_18_1
  doi: 10.1186/s13640-017-0179-0
– ident: e_1_3_3_8_1
  doi: 10.1016/j.image.2019.115713
– ident: e_1_3_3_29_1
  doi: 10.1016/j.dsp.2012.11.002
– ident: e_1_3_3_34_1
  doi: 10.1109/TGRS.2022.3231215
– ident: e_1_3_3_36_1
  doi: 10.1007/s11042-012-1188-8
– ident: e_1_3_3_15_1
  doi: 10.1109/IGARSS47720.2021.9553872
– ident: e_1_3_3_4_1
  doi: 10.1007/s11042-015-2851-7
– volume: 48
  start-page: 448
  year: 2019
  ident: e_1_3_3_17_1
  article-title: Building extraction via convolutional neural networks from an open remote sensing building dataset
  publication-title: Acta Geod Cartographica Sin
– ident: e_1_3_3_24_1
  doi: 10.1109/TCYB.2019.2914351
– volume: 36
  start-page: 1405
  issue: 7
  year: 2008
  ident: e_1_3_3_26_1
  article-title: An overview of perceptual hashing
  publication-title: Acta Electronica Sinica
– ident: e_1_3_3_20_1
  doi: 10.1007/s11042-018-5799-6
– volume: 42
  start-page: 2999
  year: 2017
  ident: e_1_3_3_21_1
  article-title: Focal loss for dense object detection
  publication-title: IEEE Trans Pattern Anal Mach Intell
– ident: e_1_3_3_25_1
  doi: 10.1109/ICMEW.2017.8026309
– ident: e_1_3_3_19_1
  doi: 10.1186/s40064-016-3639-6
– ident: e_1_3_3_33_1
  doi: 10.1109/TPAMI.2011.103
– ident: e_1_3_3_43_1
  doi: 10.3390/ijgi9040254
– ident: e_1_3_3_47_1
  doi: 10.1109/TGRS.2020.2994057
– ident: e_1_3_3_38_1
  doi: 10.1109/TPAMI.2012.48
– year: 2019
  ident: e_1_3_3_2_1
  article-title: FPD-M-net: fingerprint image denoising and inpainting using M-Net based convolutional neural networks
  publication-title: arXiv preprint arXiv:1812.10191.
– ident: e_1_3_3_41_1
  doi: 10.1109/TMI.2020.3010102
– volume: 47
  start-page: 2226
  issue: 9
  year: 2021
  ident: e_1_3_3_22_1
  article-title: Unsupervised image-to-image translation with self-attention and relativistic discriminator adversarial networks
  publication-title: Acta Autom Sin
– ident: e_1_3_3_5_1
  doi: 10.3390/rs13245109
– year: 2018
  ident: e_1_3_3_27_1
  article-title: Attention u-net: learning where to look for the pancreas
  publication-title: arXiv preprint arXiv:1804.03999.
– ident: e_1_3_3_44_1
  doi: 10.1109/JSTSP.2019.2955012
– ident: e_1_3_3_23_1
  doi: 10.1109/TGRS.2019.2907932
– ident: e_1_3_3_13_1
  doi: 10.1016/j.neunet.2019.08.025
– ident: e_1_3_3_45_1
  doi: 10.1088/1755-1315/440/3/032138
– ident: e_1_3_3_9_1
  doi: 10.1109/TMI.2018.2791488
– ident: e_1_3_3_46_1
  doi: 10.1109/ICSIDP47821.2019.9173237
– ident: e_1_3_3_14_1
  doi: 10.1109/DDCLS49620.2020.9275068
SSID ssj0037863
Score 2.3174305
Snippet The premise of effective use of high-resolution remote sensing (HRRS) images is that the data integrity and authenticity of HRRS images must be guaranteed....
SourceID doaj
proquest
crossref
informaworld
SourceType Open Website
Aggregation Database
Enrichment Source
Index Database
Publisher
SubjectTerms algorithms
attention gate
Attention mechanism
exhibitions
extracts
gates
image analysis
integrity authentication
perceptual hashing
principal component analysis
remote sensing
subject-sensitive hashing
tampering
U-net
SummonAdditionalLinks – databaseName: DOAJ Open Access Full Text
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwELYQUkUvVQutum1BrsQ1S2I7dnykCLqtRA88JE61JrHdRYJsxWYP_Htm4gRBOeylVytWRuPxvDL5Psb2TY23KLcm80pggSKBiNyVymotFboGH2OgfsfpLz27VD-vyqsnVF80E5bggZPiDvLa10IZHwwYBTq3oCJgCdXEWMqmjOR9MeaNxVTywdJUOo3Wo5fRmASP_-5U-QGt0dKUiMOnotBVbopnUakH7_8HuvSFq-7jz8lb9mZIHPlhEvgd2wjtNtsaOMzn99vsVT_M2Sx32O_D7z9OszZ0nGKU58tVTd2WbEnD6uTe-DxRKHG4-bO4u-7mtxxl4Ak6AtNyDjT33nZDP48vIp-dnZ3z61v0Psv37PLk-OJolg08ClmD6VGXARahHkN_xGpORC8bAC0aFUBUoKA0BRgQjSyDtV6HoLRXoSLFeC1ywmf_wDbbRRs-Mh68zLGcDjZgGmK1sL4orILcCh-wcFITpkY9umYAGSeuixtXDFiko_odqd8N6p-w6eO2vwllY92Gb3RIjw8TSHa_gKbjBtNx60xnwuzTI3Zd3yOJidDEyTUCfB3tweGFpK8s0IbFCvehW5SYVFv56X8I-Zm9pvcmbMkvbLO7W4VdzIO6eq83-QdbL_22
  priority: 102
  providerName: Directory of Open Access Journals
Title AGIM-net based subject-sensitive hashing algorithm for integrity authentication of HRRS images
URI https://www.tandfonline.com/doi/abs/10.1080/10106049.2023.2168071
https://www.proquest.com/docview/3040377493
https://doaj.org/article/0bdb247de7a74a609a4fa132cff53c5f
Volume 38
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3BbtQwELVQUQUXRAuIBbpyJa6pEttx1seC2G6RyqGwElywJrG9W6lNqk320L_vTOJUQFX1wClKFFvReOZ5ZjJ-w9jHokQrSk2ROCUwQJFAjdyVSkotFUKDC8FTvuPsm14s1def-VhN2MaySoqhw0AU0WM1GTeU7VgRh9eMKF_omImQRyLTs5ROkT8VpK2o0umvxQjGspjpocYe4YbGjId4Hprmr-2pZ_H_h8P0Hmb3G9H8JXsRPUh-PCz5Hnvi6332LDYzX9_ss92-qrNqX7HfxyenZ0ntO06blePttqS0S9JS1TrhHF8PvZQ4XK6azUW3vuL4DXzgkED_nAMVwNddTOzxJvDF-fl3fnGFMNS-Zsv5lx-fF0lsqJBU6Cd1CWA06tAHCBjWieBkBaBFpTyIGSjIiwwKEJXMvTFOe6-0U35GgnFapETU_obt1E3t3zLunUwxrvbGoz9itDAuy4yC1AjnMYJSE6ZGOdoqso1T04tLm0VS0lH8lsRvo_gn7Ohu2PVAt_HYgE-0SHcvE1t2_6DZrGw0PpuWrhSqcL6AQoFODagAGIZXIeSyysOEmT-X2HZ9siQMnU2sfOQDDkd9sGiZ9LsFat9scRzio0Tv2sh3_zH_e_acbgduyQ9sp9ts_QH6QV057TV92mcRbgFN8fo7
linkProvider Taylor & Francis
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZQESqXUgqI5WkkrlkltmOvjwXRbqG7h9JKPWE5fnQr2qTazR7g1zMTJ1UpQj30FCmJo4wz83lmMv6GkI-qAivKtcq8YBCgcIuN3IXIKskFQIOPMWC-YzaX0xPx9bQ8vbEXBssqMYaOiSiiw2o0bkxGDyVxcCyQ8wX3mTA-ZoWc5LiN_GGppcIuBjyfD2jM1USmInvAGxwz7OL532P-Wp86Gv9bJKb_gHa3Eu09IW6QIRWg_Byv22rsft-id7yfkNtkq3dU6W7SrKfkQah3yGbfM33xa4c86opH3eoZ-bG7fzDL6tBSXBM9Xa0rzO5kKyyORzili9SyidqLs2Z53i4uKUhKE1UFhAHUYp193fb5Q9pEOj06-k7PLwHtVs_Jyd6X48_TrO_bkDlwx9rMQtDrwdWIED2y6LmzVjIngmUTK2ypCqssc7wMWnsZgpBehAkK6CXLkQ_-Bdmomzq8JDR4nkP4HnQAt0dLpn1RaGFzzXyAQE2MiBi-lnE9qTn21rgwRc99OkyjwWk0_TSOyPh62FVi9bhrwCdUheubkZS7O9Esz0xv4yavfMWE8kFZJazMtRXRQrTvYiy5K-OI6JuKZNouJxNTAxXD73iBD4PWGQAA_Ktj69CsYRzAMAcnXvNX93j-e7I5PZ4dmsOD-bfX5DFeSnSWb8hGu1yHt-B6tdW7zrb-AGytHMI
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELZQEY8Lj0LF8jQS16wS23HWx_JYtkBXqFCJE5bjR7eiTapN9gC_npnYqaAI9dBTpCQTZZyZ8cxk5htCXlU1aFGuqswJBgEKNzjIXYisllyAaXAheMx37C_l4lB8-FaO1YRdKqvEGDpEoIjBVqNyn7kwVsTBsUDIF2wzYXzKCjnLsYv8ukTwcOziyJejMebVTMYaezA3SDM28fzvMX9tTwOK_wUM039s9rARze-SemQh1p_8mG76emp_XUB3vBKP98id5KbS3ShX98k132yTW2li-urnNrkxlI7a7gH5vvt-bz9rfE9xR3S029SY28k6LI1HY0pXcWATNSdH7fq4X51SYJRGoAoIAqjBKvumT9lD2ga6ODj4Qo9PwdZ1D8nh_N3XN4ssTW3ILDhjfWYg5HXgaASIHVlw3BojmRXesJkRpqwKUxlmeemVctJ7IZ3wM2TQSZYjGvwO2Wraxj8i1DueQ_DulQenR0mmXFEoYXLFnIcwTUyIGD-WtgnSHCdrnOgiIZ-Oy6hxGXVaxgmZnpOdRUyPywheoySc34yQ3MOJdn2kk4brvHY1E5XzlamEkbkyIhiI9W0IJbdlmBD1pxzpfsjIhDg-RfNLXuDlKHQa1B__6ZjGtxugAyPMwYVX_PEVnv-C3Pz8dq4_7S0_PiG38UrEsnxKtvr1xj8Dv6uvnw-a9RvCnRtm
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=AGIM-net+based+subject-sensitive+hashing+algorithm+for+integrity+authentication+of+HRRS+images&rft.jtitle=Geocarto+international&rft.au=Ding%2C+Kaimeng&rft.au=Zeng%2C+Yue&rft.au=Wang%2C+Yingying&rft.au=Lv%2C+Dong&rft.date=2023-12-31&rft.pub=Taylor+%26+Francis&rft.issn=1010-6049&rft.eissn=1752-0762&rft.volume=38&rft.issue=1&rft_id=info:doi/10.1080%2F10106049.2023.2168071&rft.externalDBID=0YH&rft.externalDocID=2168071
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1010-6049&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1010-6049&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1010-6049&client=summon