Reversible Data Hiding Scheme During Encryption Using Machine Learning

Manikandan, V. M.; Masilamani, V.

doi:10.1016/j.procs.2018.07.043

Cited by 43 publications

(25 citation statements)

References 18 publications

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“…In medicine, RDH methods are often used to enrich medical images with sections of the EPR (Electronic Patient Record), which is a digital version of a patient's paper chart (medical history, disease diagnosis, treatment methods, etc.) [12,13]. Additionally, RDH methods are applied not only to images, but also to video signals [14,15] and sound signals [16,17].…”

Section: Related Workmentioning

confidence: 99%

“…Because of the detailed comparison with other existing RDHEI methods, already shown in [2], we treated [2] as a reference for our comparison analysis. To the best of our knowledge, this is the first RDHEI method to use the cyclic binary Golay (23,12) code for data embedding. The contribution of this paper is as follows.…”

Section: Introductionmentioning

confidence: 99%

“…• A cyclic binary Golay (23,12) coding scheme, exploiting the properties of syndromes and error vectors to enable a high embedding capacity and quality of the marked data. • A data recovery scheme, which enables a reconstruction of the original data (error-free) and recovery of the embedded data (before or after decryption).…”

Section: Introductionmentioning

confidence: 99%

“…An example concept of a modified DICOM network with applied RDHEI mechanisms is shown in Section 3. Section 4 contains a brief description of the cyclic binary Golay (23,12) code, its properties and coding rules. The proposed RDHEI method, with details on data processing, data embedding and data recovery is described in Section 5.…”

Section: Introductionmentioning

confidence: 99%

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Reversible Data Hiding in Encrypted DICOM Images Using Cyclic Binary Golay (23, 12) Code

Dzwonkowski

Rykaczewski

2021

IEEE Access

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In this paper, a novel reversible data hiding method for encrypted images (RDHEI) is proposed. An efficient coding scheme based on cyclic binary Golay (23, 12) code is designed to embed additional data into the least significant bits (LSBs) of the encrypted image. The most significant bits (MSBs) are used to ensure the reversibility of the embedding process. The proposed scheme is lossless, and based on the receiver's privileges, allows recovery of marked data, original data and embedded data. Furthermore, the scheme can be used with any type of data, however it is best suited to 16-bit DICOM images of monochrome photometric interpretation. A modification to the standard DICOM network model was also introduced, to point out an example application of the proposed RDHEI method, i.e. an anonymized data storage outsourcing. A computer-based analysis has been carried out and simulation results are shown at the end of this paper.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reversible Data Hiding in Encrypted DICOM Images Using Cyclic Binary Golay (23, 12) Code

Dzwonkowski

Rykaczewski

2021

IEEE Access

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“…In this method, the primary information was transformed into the customized data container before being encrypted with the diffractiveimaging-based encryption. A reversible data hiding scheme through the image encryption and support vector machine based data extraction and image recovery scheme was proposed in [19]. This scheme provides a way for a secure medical image transmission.…”

Section: Introductionmentioning

confidence: 99%

A New and Adaptive Security Model for Public Communication Based on Correlation of Data Frames

et al. 2019

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Recent developments in communication and information technologies, plus the emerging of the Internet of Things (IoT) and machine to machine (M2M) principles, create the need to protect data from multiple types of attacks. In this paper, a secure and high capacity data communication model is proposed to protect the transmitted data based on identical frames between a secret and cover data. In this model, the cover data does not convey any embedded data (as in normal steganography system) or modify the secret message (as in traditional cryptography techniques). Alternatively, the proposed model sends the positions of the cover frames that are identical with the secret frames to the receiver side in order to recover the secret message. One of the significant advantages of the proposed model is the size of the secret key message which is considerably larger than the cover size, it may be even hundred times larger. Accordingly, the experimental results demonstrate a superior performance in terms of the capacity rate as compared to the traditional steganography techniques. Moreover, it has an advantage in terms of the required bandwidth to send the data or the required memory for saving when compared to the steganography methods, which need a bandwidth or memory up to 3-5 times of the original secret message. Where the length of the secret key (positions of the identical frames) that should be sent to the receiver increases by only 25% from the original secret message. This model is suitable for applications with a high level of security, high capacity rate and less bandwidth of communication or low storage devices.

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Electronic Medical Information Encryption Using Modified Blowfish Algorithm

Oluwatobi

Abikoye

Adebiyi

et al. 2019

Computational Science and Its Applications – ICCSA 2019

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Security and privacy of patients' information remains a major issue of concern among health practitioners. Therefore, measures must be put in place to ensure that unauthorized individual do not have access to this information. However, the adoption of digital alternative of retrieving and documenting medical information has further opened it up to more attacks. This article presents a modified blowfish algorithm for securing textual and graphical medical information. The F-function used in generating round sub-keys was strengthened so as to produce a strong key that could resist differential attacks. Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI) of 98.85% and 33.65% revealed that the modified algorithm is sensitive to changes in its key and also resistive to differential attacks. Furthermore, the modified algorithm demonstrated a better encryption and decryption time than the existing blowfish algorithm.

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Reversible Data Hiding Scheme During Encryption Using Machine Learning

Cited by 43 publications

References 18 publications

Reversible Data Hiding in Encrypted DICOM Images Using Cyclic Binary Golay (23, 12) Code

Reversible Data Hiding in Encrypted DICOM Images Using Cyclic Binary Golay (23, 12) Code

A New and Adaptive Security Model for Public Communication Based on Correlation of Data Frames

Electronic Medical Information Encryption Using Modified Blowfish Algorithm

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