Exploiting Laguerre transform in image steganography

Ghosal, Sudipta Kr; Mukhopadhyay, Souradeep; Hossain, Sabbir; Sarkar, Ram

doi:10.1016/j.compeleceng.2020.106964

Cited by 20 publications

(13 citation statements)

References 10 publications

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“…Five different methods such as FSO‐PSO, IORA, JDICA, RFID‐WSN, and proposed with 100 to 500 sensor nodes are used. When compared to the existing methods, the proposed methodology provides less false alarm rate to the effective normal, black hole, and gray hole attack detection 43,44 . Based on the cooperative count, trust value, and data transmission using mutation data, the proposed technique successfully investigates each sensor characteristic in the H‐WSN.…”

Section: Resultsmentioning

confidence: 99%

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Subasini

Karuppiah

Sheeba

et al. 2021

Trans Emerging Tel Tech

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Attack detection is the major issue in healthcare‐based wireless sensor networks (H‐WSNs). Due to their low processing speed, very low storage space, poor attack detection rate, longer deployment time, poor communication range, and reduced energy, H‐WSNs are subjected to difficult implementation and have their own limitations. To tackle these issues, we have presented a hybrid deep learning model using convolutional neural network and long short term memory (HDMCL) for attack detection in H‐WSN. This research is divided into three steps: preprocessing, dimensionality reduction, and classification (attack detection). At first, the raw input data (patient's health data) is preprocessed using the one‐hot encoding method. Next, the modified Huber independent component analysis based squirrel search algorithm (MHICA‐SSA) effectively reduces the data dimensionality in which the MHICA‐SSA is the amalgamation of both modified Huber independent component analysis and squirrel search algorithm. The novel algorithm designed overcomes the complexities associated with existing techniques such as the curse of dimensionality problem, improves the parameter interpretation, minimizes the time and storage space, minimizes space complexity, and enhances the attack detection accuracy of the convolutional neural network‐based long short‐term memory (CNN‐LSTM). After that, the deep learning CNN‐LSTM model is utilized for normal, black hole, and gray hole attack detection. The NS2.34 network simulator implements the proposed work thereby the proposed work efficiency is validated using different performance measures such as packet delivery ratio, false alarm rate, network lifetime, energy consumption, throughput, attack detection time, and rate. The proposed work performance is evaluated using different existing methods such as fish swarm optimization based particle swarm optimization, intelligent opportunistic routing algorithm, Jensen‐Shannon divergence‐based independent component analysis, and radio frequency identification based WSN. The throughput of the proposed work is increased up to 3.2 times and the network lifetime is increased up to 4 times when compared to the existing techniques.

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Section: Resultsmentioning

confidence: 99%

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Subasini

Karuppiah

Sheeba

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…In Even-Odd based data hiding, the parity of pixel value [11][12][13] or quantized high-frequency coefficient [14][15][16][17][18] is used to correspond to the bit to be hidden. Like LSB technique, a single pixel or quantized transform coefficient is used as the carrier of information, and its parity is directly used to represent the hidden bit.…”

Section: Fig 1 a Simple Model Of Information Hidingmentioning

confidence: 99%

Large-Capacity Image Data Hiding based on Table Look-up

Ding¹,

Zhang²,

Reulke³

et al. 2021

Preprint

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In previous data hiding techniques, binary rules are usually used to guide the fine-tuning of the values of basic objects in the host media to hide bit 0 and bit 1. In this paper, we propose a new data hiding technique for gray images based on querying a 256x256 information table. The information table is constructed by cloning a 3x3 basic block, which we call seed block. Eight unsigned integer values between 0 and 7, i.e., 3 bit binary data, are assigned to different elements of the seed block. Each time, a pair of pixels are chosen from a host image, and their pixel values are used as row and column numbers to look up the information table. If element value obtained by looking up the table is equal to the 3 bit binary data to be hidden, the values of the pixel pair will remain unchanged. Otherwise, take this element as the central point, we call it the focus element, to enclose a 3x3 window in the information table. Then in the window, find the element which is equal to the data to be hidden. Finally, update the pixel values of the pair with the row and column numbers of the found element in the window. Since the row and column numbers are in the range of 0-255, the updated pixel values will not overflow. In the proposed algorithm, a pair of pixels can hide 3 bits of information, so the embedding capacity is very high. Since the adjustment of pixel values is constrained in a 3x3 window, the modification amount of pixel values is small. The proposed technique belongs to fragile digital watermarking, so it can be used for image authentication and tamper localization. By the evaluation of data hiding capacity, security, imperceptibility, computational cost and extensibility, this algorithm is superior to existing information hiding techniques. The proposed technique can also be used in color image and audio data hiding.

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“…The data to be hidden in the transform domain is hidden in the cover multimedia using transposition. The most familiar methods are Discrete Cosine Transform (DCT) [31,32], Discrete Haar Wavelet Transform (DWT) [33], and Discrete Fourier Transform (DFT). Considering that the cover used in DCT will be an image, it divides the image into 8 × 8 blocks.…”

Section: Steganography: An Overviewmentioning

confidence: 99%

A Novel and Robust Hybrid Blockchain and Steganography Scheme

et al. 2021

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Data security and data hiding have been studied throughout history. Studies show that steganography and encryption methods are used together to hide data and avoid detection. Large amounts of data hidden in the cover multimedia distort the image, which can be detected in visual and histogram analysis. The proposed method will solve two major drawbacks of the current methods: the limitation imposed on the size of the data to be hidden in the cover multimedia and low resistance to steganalysis after stego-operation. In the proposed method, plaintext data are divided into fixed-sized bits whose corresponding matching bits’ indices in the cover multimedia are accumulated. Thus, the hidden data are composed of the indices in the cover multimedia, causing no change in it, thus enabling considerable amounts of plaintext to be hidden. The proposed method also has high resistance to known steganalysis methods because it does not cause any distortion to the cover multimedia. The test results show that the performance of the proposed method outperforms similar conventional stenographic techniques. The proposed Ozyavas–Takaoglu–Ajlouni (OTA) method relieves the limitation on the size of the hidden data, and hidden data is undetectable by steganalysis because it is no longer embedded in the cover multimedia.

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Exploiting Laguerre transform in image steganography

Cited by 20 publications

References 10 publications

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Developing an attack detection framework for wireless sensor network‐based healthcare applications using hybrid convolutional neural network

Large-Capacity Image Data Hiding based on Table Look-up

A Novel and Robust Hybrid Blockchain and Steganography Scheme

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