Data hiding in virtual bit-plane using efficient Lucas number sequences

Datta, Bithin; Dutta, Koushik; Roy, Sudipta

doi:10.1007/s11042-020-08979-3

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…-Every positive integer can be represented uniquely as the sum of one or more non-consecutive distinct Fibonacci numbers‖ [99]. Hence, the number representation is considered valid if no consecutive ones appear in the sequence.…”

Section: Bit-plane Systemmentioning

confidence: 99%

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A Review on Security Techniques in Image Steganography

Ghoul¹,

Sulaiman²,

Shukur³

2023

IJACSA

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Given the increased popularity of the internet, the exchange of sensitive information leads to concerns about privacy and security. Techniques such as steganography and cryptography have been employed to protect sensitive information. Steganography is one of the promising tools for securely exchanging sensitive information through an unsecured medium. It is a powerful tool for protecting a user's data, wherein the user can hide messages inside other media, such as images, videos, and audios (cover media). Image steganography is the science of concealing secret information inside an image using various techniques. The nature of the embedding process makes the hidden information undetectable to human eyes. The challenges faced by image steganography techniques include achieving high embedding capacity, good imperceptibility, and high security. These criteria are inter-related since enhancing one factor undermines one or more others. This paper provides an overview of existing research related to various techniques and security in image steganography. First, basic information in this domain is presented. Next, various kinds of security techniques used in steganography are explained, such as randomization, encryption, and region-based techniques. This paper covers research published from 2017 to 2022. This review is not exhaustive and aims to explore state-of-the-art techniques applied to enhance security, crucial issues in the domain, and future directions to assist new and current researchers.

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Section: Bit-plane Systemmentioning

confidence: 99%

“…In the scheme presented in [99], secret data is embedded in different bit-planes rather than using the regular binary bit planes. It is based on the Lucas Number system that uses 11 numbers for representations.…”

Section: Bit-plane Systemmentioning

confidence: 99%

A Review on Security Techniques in Image Steganography

Ghoul¹,

Sulaiman²,

Shukur³

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Although these models are capable of outperforming humans in terms of efficiency, it is difficult to provide intuitive interpretations which can validate the findings of the model, define their uncertainties, or derive further clinical understandings from these computational algorithms. With millions of attributes in the DL model, understanding what the machine sees in clinical data, such as radiographical images and dermatoscopic images, can indeed be very difficult [ 124 , 125 ]. It is important to show that a high-performance DL model properly recognizes the relevant portion of the image and fails to overemphasize irrelevant data (See Figure 10 ).…”

Section: Explainability In Healthcarementioning

confidence: 99%

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Roy¹,

Meena²,

Lim

2022

Diagnostics

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The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods. This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. The effectiveness and potential for innovation of disease diagnosis, personalized medicine, clinical trials, non-invasive image analysis, drug discovery, patient care services, remote patient monitoring, hospital data, and nanotechnology in various learning-based automation in healthcare along with the requirement for explainable artificial intelligence (AI) in healthcare are evaluated. In order to understand the potential architecture of non-invasive treatment, a thorough study of medical imaging analysis from a technical point of view is presented. This study also represents new thinking and developments that will push the boundaries and increase the opportunity for healthcare through AI and SML in the near future. Nowadays, SML-based applications require a lot of data quality awareness as healthcare is data-heavy, and knowledge management is paramount. Nowadays, SML in biomedical and healthcare developments needs skills, quality data consciousness for data-intensive study, and a knowledge-centric health management system. As a result, the merits, demerits, and precautions need to take ethics and the other effects of AI and SML into consideration. The overall insight in this paper will help researchers in academia and industry to understand and address the future research that needs to be discussed on SML in the healthcare and biomedical sectors.

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Image encryption using a novel hybrid chaotic map and dynamic permutation−diffusion

Kumar,

Dua

2023

Multimed Tools Appl

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Data hiding in virtual bit-plane using efficient Lucas number sequences

Cited by 4 publications

References 27 publications

A Review on Security Techniques in Image Steganography

A Review on Security Techniques in Image Steganography

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine

Image encryption using a novel hybrid chaotic map and dynamic permutation−diffusion

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