An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform
Reversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without any deformation, along with recov...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/2/450 |
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| author | Shaiju Panchikkil Siva Priya Vegesana V. M. Manikandan Praveen Kumar Donta Praveen Kumar Reddy Maddikunta Thippa Reddy Gadekallu |
| author_facet | Shaiju Panchikkil Siva Priya Vegesana V. M. Manikandan Praveen Kumar Donta Praveen Kumar Reddy Maddikunta Thippa Reddy Gadekallu |
| author_sort | Shaiju Panchikkil |
| collection | DOAJ |
| description | Reversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without any deformation, along with recovery of the hidden secret. In this paper, a new RDH scheme is proposed for performing reversible data hiding in encrypted images using a Fibonacci transform with an ensemble learning method. In the proposed scheme, the data hider encrypts the original image and performs further data hiding. During data hiding, the encrypted image is partitioned into non-overlapping blocks, with each block considered one-by-one. The selected block undergoes a series of Fibonacci transforms during data hiding. The number of Fibonacci transforms required on a selected block is determined by the integer value that the data hider wants to embed. On the receiver side, message extraction and image restoration are performed with the help of the ensemble learning method. The receiver will try to perform all possible Fibonacci transforms and decrypt the blocks. The recovered block is identified with the help of trained machine-learning models. The novelty of the scheme lies in (1) retaining the encrypted pixel intensities unaltered while hiding the data. Almost every RDH scheme described in the literature alters the encrypted pixel intensities to embed the data, which represents a security concern for the encryption algorithm; (2) Introducing an efficient means of recovery through an ensemble model framework. The majority of votes from the different trained models guarantee the correct recovery of the cover image. The proposed scheme enables reduction in the bit error rate during message extraction and contributes to ensuring the suitability of the scheme in areas such as medical image transmission and cloud computing. The results obtained from experiments undertaken show that the proposed RDH scheme was able to attain an improved payload capacity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.0625</mn></mrow></semantics></math></inline-formula> bits per pixel, outperforming many related RDH schemes with complete reversibility. |
| first_indexed | 2024-03-09T12:56:22Z |
| format | Article |
| id | doaj.art-e524cb4c7962463d8a037d55ed435c8d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T12:56:22Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e524cb4c7962463d8a037d55ed435c8d2023-11-30T22:00:38ZengMDPI AGElectronics2079-92922023-01-0112245010.3390/electronics12020450An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci TransformShaiju Panchikkil0Siva Priya Vegesana1V. M. Manikandan2Praveen Kumar Donta3Praveen Kumar Reddy Maddikunta4Thippa Reddy Gadekallu5Department of Computer Science and Engineering, SRM University-AP, Amaravati 522502, IndiaDepartment of Computer Science and Engineering, SRM University-AP, Amaravati 522502, IndiaDepartment of Computer Science and Engineering, SRM University-AP, Amaravati 522502, IndiaDistributed Systems Group, TU Wien, 1040 Vienna, AustriaSchool of Information Technology & Engineering, Vellore Institute of Technology, Vellore 632014, IndiaSchool of Information Technology & Engineering, Vellore Institute of Technology, Vellore 632014, IndiaReversible data hiding (RDH) is an active area of research in the field of information security. In RDH, a secret can be embedded inside a cover medium. Unlike other data-hiding schemes, RDH becomes important in applications that demand recovery of the cover without any deformation, along with recovery of the hidden secret. In this paper, a new RDH scheme is proposed for performing reversible data hiding in encrypted images using a Fibonacci transform with an ensemble learning method. In the proposed scheme, the data hider encrypts the original image and performs further data hiding. During data hiding, the encrypted image is partitioned into non-overlapping blocks, with each block considered one-by-one. The selected block undergoes a series of Fibonacci transforms during data hiding. The number of Fibonacci transforms required on a selected block is determined by the integer value that the data hider wants to embed. On the receiver side, message extraction and image restoration are performed with the help of the ensemble learning method. The receiver will try to perform all possible Fibonacci transforms and decrypt the blocks. The recovered block is identified with the help of trained machine-learning models. The novelty of the scheme lies in (1) retaining the encrypted pixel intensities unaltered while hiding the data. Almost every RDH scheme described in the literature alters the encrypted pixel intensities to embed the data, which represents a security concern for the encryption algorithm; (2) Introducing an efficient means of recovery through an ensemble model framework. The majority of votes from the different trained models guarantee the correct recovery of the cover image. The proposed scheme enables reduction in the bit error rate during message extraction and contributes to ensuring the suitability of the scheme in areas such as medical image transmission and cloud computing. The results obtained from experiments undertaken show that the proposed RDH scheme was able to attain an improved payload capacity of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.0625</mn></mrow></semantics></math></inline-formula> bits per pixel, outperforming many related RDH schemes with complete reversibility.https://www.mdpi.com/2079-9292/12/2/450Fibonacci transformreversible data hidingdata extraction and image recoveryinformation hiding |
| spellingShingle | Shaiju Panchikkil Siva Priya Vegesana V. M. Manikandan Praveen Kumar Donta Praveen Kumar Reddy Maddikunta Thippa Reddy Gadekallu An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform Electronics Fibonacci transform reversible data hiding data extraction and image recovery information hiding |
| title | An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform |
| title_full | An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform |
| title_fullStr | An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform |
| title_full_unstemmed | An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform |
| title_short | An Ensemble Learning Approach for Reversible Data Hiding in Encrypted Images with Fibonacci Transform |
| title_sort | ensemble learning approach for reversible data hiding in encrypted images with fibonacci transform |
| topic | Fibonacci transform reversible data hiding data extraction and image recovery information hiding |
| url | https://www.mdpi.com/2079-9292/12/2/450 |
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