Reversible Data Hiding Scheme During Encryption Using Machine Learning Article Swipe
V. M. Manikandan
,
V. Masilamani
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2018.07.043
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2018.07.043
Reversible data hiding (RDH) is a recent research field of information security for secured digital data transmission. The advancements in communication technology and the invention of new medical robotics are very much useful in telemedicine applications. The transmission of medical image and electronic patient records (EPR) is a common process in telemedicine. The images captured by robots may need to be authenticated, the RDH schemes can be used to authenticate data and/or the owner of the data. In addition, the RDH techniques provide a way to embed EPR data into medical images before transmission. The EPR data extraction and recovery of the original image can be carried out by the receiver. This manuscript proposes a new RDH scheme to embed EPR data during image encryption process. A block-wise image encryption technique has been used in the proposed scheme to obtain the encrypted image with hidden EPR data bits. The novelty of the proposed scheme is that a support vector machine (SVM) based classification scheme has been used for data extraction and image recovery process from the encrypted image. Experimental study of the proposed scheme on standard medical images from OsriX dataset shows that the proposed scheme performs better than the existing schemes in terms of embedding rate and bit error rate.
Related Topics
Concepts
Computer science
Encryption
Artificial intelligence
Scheme (mathematics)
Support vector machine
Data extraction
Information hiding
Block (permutation group theory)
Process (computing)
Field (mathematics)
Transmission (telecommunications)
Image (mathematics)
Computer vision
Computer security
Political science
MEDLINE
Telecommunications
Pure mathematics
Mathematics
Operating system
Law
Geometry
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2018.07.043
- OA Status
- diamond
- Cited By
- 45
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2883094232
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2883094232Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2018.07.043Digital Object Identifier
- Title
-
Reversible Data Hiding Scheme During Encryption Using Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
V. M. Manikandan, V. MasilamaniList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2018.07.043Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.procs.2018.07.043Direct OA link when available
- Concepts
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Computer science, Encryption, Artificial intelligence, Scheme (mathematics), Support vector machine, Data extraction, Information hiding, Block (permutation group theory), Process (computing), Field (mathematics), Transmission (telecommunications), Image (mathematics), Computer vision, Computer security, Political science, MEDLINE, Telecommunications, Pure mathematics, Mathematics, Operating system, Law, Geometry, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
45Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 8, 2022: 9, 2021: 12Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.In | 77 |
| abstract_inverted_index.be | 60, 66, 105 |
| abstract_inverted_index.by | 55, 108 |
| abstract_inverted_index.in | 19, 33, 50, 134, 202 |
| abstract_inverted_index.is | 4, 46, 154 |
| abstract_inverted_index.of | 9, 25, 38, 74, 100, 150, 180, 204 |
| abstract_inverted_index.on | 184 |
| abstract_inverted_index.to | 59, 68, 85, 118, 138 |
| abstract_inverted_index.EPR | 87, 95, 120, 145 |
| abstract_inverted_index.RDH | 63, 80, 116 |
| abstract_inverted_index.The | 17, 36, 52, 94, 148 |
| abstract_inverted_index.and | 22, 41, 98, 170, 207 |
| abstract_inverted_index.are | 29 |
| abstract_inverted_index.bit | 208 |
| abstract_inverted_index.can | 65, 104 |
| abstract_inverted_index.for | 12, 167 |
| abstract_inverted_index.has | 131, 164 |
| abstract_inverted_index.may | 57 |
| abstract_inverted_index.new | 26, 115 |
| abstract_inverted_index.out | 107 |
| abstract_inverted_index.the | 23, 62, 72, 75, 79, 101, 109, 135, 140, 151, 175, 181, 193, 199 |
| abstract_inverted_index.way | 84 |
| abstract_inverted_index.This | 111 |
| abstract_inverted_index.been | 132, 165 |
| abstract_inverted_index.data | 1, 15, 70, 88, 96, 121, 146, 168 |
| abstract_inverted_index.from | 174, 188 |
| abstract_inverted_index.into | 89 |
| abstract_inverted_index.much | 31 |
| abstract_inverted_index.need | 58 |
| abstract_inverted_index.rate | 206 |
| abstract_inverted_index.than | 198 |
| abstract_inverted_index.that | 155, 192 |
| abstract_inverted_index.used | 67, 133, 166 |
| abstract_inverted_index.very | 30 |
| abstract_inverted_index.with | 143 |
| abstract_inverted_index.(EPR) | 45 |
| abstract_inverted_index.(RDH) | 3 |
| abstract_inverted_index.(SVM) | 160 |
| abstract_inverted_index.OsriX | 189 |
| abstract_inverted_index.based | 161 |
| abstract_inverted_index.bits. | 147 |
| abstract_inverted_index.data. | 76 |
| abstract_inverted_index.embed | 86, 119 |
| abstract_inverted_index.error | 209 |
| abstract_inverted_index.field | 8 |
| abstract_inverted_index.image | 40, 103, 123, 128, 142, 171 |
| abstract_inverted_index.owner | 73 |
| abstract_inverted_index.rate. | 210 |
| abstract_inverted_index.shows | 191 |
| abstract_inverted_index.study | 179 |
| abstract_inverted_index.terms | 203 |
| abstract_inverted_index.and/or | 71 |
| abstract_inverted_index.before | 92 |
| abstract_inverted_index.better | 197 |
| abstract_inverted_index.common | 48 |
| abstract_inverted_index.during | 122 |
| abstract_inverted_index.hidden | 144 |
| abstract_inverted_index.hiding | 2 |
| abstract_inverted_index.image. | 177 |
| abstract_inverted_index.images | 53, 91, 187 |
| abstract_inverted_index.obtain | 139 |
| abstract_inverted_index.recent | 6 |
| abstract_inverted_index.robots | 56 |
| abstract_inverted_index.scheme | 117, 137, 153, 163, 183, 195 |
| abstract_inverted_index.useful | 32 |
| abstract_inverted_index.vector | 158 |
| abstract_inverted_index.carried | 106 |
| abstract_inverted_index.dataset | 190 |
| abstract_inverted_index.digital | 14 |
| abstract_inverted_index.machine | 159 |
| abstract_inverted_index.medical | 27, 39, 90, 186 |
| abstract_inverted_index.novelty | 149 |
| abstract_inverted_index.patient | 43 |
| abstract_inverted_index.process | 49, 173 |
| abstract_inverted_index.provide | 82 |
| abstract_inverted_index.records | 44 |
| abstract_inverted_index.schemes | 64, 201 |
| abstract_inverted_index.secured | 13 |
| abstract_inverted_index.support | 157 |
| abstract_inverted_index.captured | 54 |
| abstract_inverted_index.existing | 200 |
| abstract_inverted_index.original | 102 |
| abstract_inverted_index.performs | 196 |
| abstract_inverted_index.process. | 125 |
| abstract_inverted_index.proposed | 136, 152, 182, 194 |
| abstract_inverted_index.proposes | 113 |
| abstract_inverted_index.recovery | 99, 172 |
| abstract_inverted_index.research | 7 |
| abstract_inverted_index.robotics | 28 |
| abstract_inverted_index.security | 11 |
| abstract_inverted_index.standard | 185 |
| abstract_inverted_index.addition, | 78 |
| abstract_inverted_index.embedding | 205 |
| abstract_inverted_index.encrypted | 141, 176 |
| abstract_inverted_index.invention | 24 |
| abstract_inverted_index.receiver. | 110 |
| abstract_inverted_index.technique | 130 |
| abstract_inverted_index.Reversible | 0 |
| abstract_inverted_index.block-wise | 127 |
| abstract_inverted_index.electronic | 42 |
| abstract_inverted_index.encryption | 124, 129 |
| abstract_inverted_index.extraction | 97, 169 |
| abstract_inverted_index.manuscript | 112 |
| abstract_inverted_index.techniques | 81 |
| abstract_inverted_index.technology | 21 |
| abstract_inverted_index.information | 10 |
| abstract_inverted_index.Experimental | 178 |
| abstract_inverted_index.advancements | 18 |
| abstract_inverted_index.authenticate | 69 |
| abstract_inverted_index.telemedicine | 34 |
| abstract_inverted_index.transmission | 37 |
| abstract_inverted_index.applications. | 35 |
| abstract_inverted_index.communication | 20 |
| abstract_inverted_index.telemedicine. | 51 |
| abstract_inverted_index.transmission. | 16, 93 |
| abstract_inverted_index.authenticated, | 61 |
| abstract_inverted_index.classification | 162 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.92172883 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |