SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

Dhawan, Sanjeev; Chakraborty, Chinmay; Frnda, Jaroslav; Gupta, Rashmi; Rana, Arun Kumar; Pani, Subhendu Kumar

doi:10.1109/access.2021.3089357

Cited by 60 publications

(9 citation statements)

References 52 publications

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“…The growing number of IoT applications needs significant processing power and expertise that even the smartest devices cannot provide; the algorithm is more exact and grounded in intermixing capability and overcomes a few inherited flaws. IoT may enhance the algorithm's inquiry capacity and search accuracy, and algorithm optimization neural network data mining models can boost data mining productivity [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Fuzzy Optimization Algorithm for Data Set Information Clustering Patterns Based on Data Mining and IoT

Chen

Zheng²

2022

Wireless Communications and Mobile Computing

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Social creation and life have become progressively unmistakable. Bunch examination is the reason for additional handling of the data. The ideas of data mining, as well as the use of neural networks in data mining, are examined. The two-layer perceptron, back engendering (BP) neural network, and RBF extended premise work network are totally depicted exhaustively in this article data mining, as demonstrated by the associated development of data mining and handling characterization issues, and self-sorting out map (SOM) is a self-assembling neural network for unaided grouping issues. As indicated by the qualities of self-versatile and self-arranging capacities, we learn, plan, and execute data mining grouping optimization techniques using these algorithms. This study isolates the neural network-based data mining technique into three phases: data course of action, rule extraction, and rule evaluation. This work centers around the teaching type and breaking down type rule extraction strategies. The connection approach is used to compute the association between the information and result neurons after researching the BP disintegration type method. After you have planned everything out, it is time to put it all together. The RBF neural network is utilized to pick center points in light of association levels. This can assist with diminishing the quantity of data centers in the neural network; further develop the network geography; decline the quantity of recursive segments in the subnet, in addition to other things; and further develop computation productivity. Accepting the model, for instance, the preparation mistake is determined through the use of data mining and a bunching algorithm. For the most part, the data mining grouping optimization method works on from two perspectives: better model planning and model pruning and rebuilding of the well-known neural network tests that duplicate model complexity, computational intricacy, and blunders. After that, the rate is calculated, and finally, the recreation investigation is carried out. The findings indicate that the proposed differential dispersed data mining algorithm is more exact and effective. More grounded combination capacity defeats the deficiencies and weaknesses of a few unique hereditary algorithms. It can truly work on the algorithm’s accessibility and search precision, as well as the usefulness of data mining employing neural network data mining models based on algorithm optimization. Precision and exactness are useful in a variety of situations. The rate is then computed, and ultimately, the reproduction study is conducted. The findings show that the proposed differential allocated data mining method works. The algorithm is more precise and more grounded in intermingling capacity and conquers the inadequacies and weaknesses of a few unique hereditary algorithms. It can successfully further improve the algorithm’s inquiry capacity and search precision, as well as work on the productivity of data mining, through algorithm optimization neural network data mining models. Precision and exactness have several applications.

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Section: Introductionmentioning

confidence: 99%

Intelligent Fuzzy Optimization Algorithm for Data Set Information Clustering Patterns Based on Data Mining and IoT

Chen

Zheng²

2022

Wireless Communications and Mobile Computing

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“…An image steganography method employing integration of several algorithms with encryption utilizing Binary bit-plane decomposition (BBPD) and adaptive embedding using Salp Swarm Optimization Algorithm (SSOA) were proposed in [4]. Finally, a hybrid Fuzzy Neural Network in addition to backpropagation learning was utilized in improving the stego image quality.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission

Ansari,

Tamilselvan

2023

IJRITCC

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Present-day evolution in communication and information technology dispenses straightforward and effortless access to data, but the most noteworthy condition is the formation of secure communication. Numerous approaches were designed for safety communication. One of the crucial approaches is image steganography. Moreover, provisioning of information security services is arrived at via cryptosystems where cryptosystems make certain the secure messages transmission between the users in an untrustworthy circumstance. The conventional method of providing encryption and signature is said to be first signing and then encryption, but both the computation and communication costs are found to be high. A certificateless signcryption mechanism is designed to transfer the medical data or images securely. This mechanism will minimize the storage and verification costs of public key certificates. The author of this article proposes a method named Lagrangian recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for transferring the medical data or images securely. In two sections the LRS-HECS method is split. They are medical image steganalysis and certificateless signcryption. First with the Chest X-Ray images obtained as input, a Codeword Correlated Lagrangian Recurrent Neural Network-based image steganography model is applied to generate steg images. Second, to transfer the medical images securely the steg images provided as input is designed a model named a Hyper Elliptic Curve-based Certificateless Signcryption. The issue of providing the integrity and validity of the transmitted medical images and receiver anonymity is addressed by the application of Hyper Elliptic Curve. Chest X-Ray pictures were used in experimental simulations, and the findings showed that the LRS-HECS approach had more advantages over existing state-of-the-art methods in terms of higher peak signal to noise ratio with data integrity and with reduced encryption time and transmission cost.

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“…Information can be protected using various methods, such as steganography, digital watermarking, reversible watermarking, encryption, and watermarking [12], [13]. This paper will focus on the technique of steganography.…”

Section: Introductionmentioning

confidence: 99%

A New Approach of Steganography on Image Metadata

Fernando,

Darwis,

Mehta

et al. 2024

JOIV : Int. J. Inform. Visualization

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In this paper, we introduce a novel method, Steganography on Image Metadata (SIM), to tackle the problem of robustness modification in steganography. The SIM method works by embedding messages into the metadata storage space of digital media. Metadata is information embedded in a file that explains the file's content. The advantage of this method is that it does not alter the pixel values in the image, ensuring no degradation in media quality, and the secret message remains secure even when robustness manipulations are applied to the stego-image. To enhance data security, this paper also suggests using Fernet cryptography for message encryption during the embedding process into the cover-image. According to experimental evaluations, the SIM technique can attain a maximum PSNR value of 100 dB and an outstanding MSE value of 0. All robustness manipulation issues in steganography can be effectively addressed using the SIM method. Test results demonstrate that the SIM method can withstand symmetric and asymmetric cropping manipulations down to a pixel size of 1x1, and the message can still be extracted. Testing with image rotation manipulation also proves that the message can be successfully extracted even when the stego-image is rotated up to 180 degrees. Experiments with image resizing manipulation also confirm that the message can be recovered even when the stego-image undergoes up to 90% compression. Testing with color effects applied to the image also does not affect message extraction results.

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SSII: Secured and High-Quality Steganography Using Intelligent Hybrid Optimization Algorithms for IoT

Cited by 60 publications

References 52 publications

Intelligent Fuzzy Optimization Algorithm for Data Set Information Clustering Patterns Based on Data Mining and IoT

Intelligent Fuzzy Optimization Algorithm for Data Set Information Clustering Patterns Based on Data Mining and IoT

Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission

A New Approach of Steganography on Image Metadata

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