A New Optimization Strategy for Solving the Fall-Off Boundary Value Problem in Pixel-Value Differencing Steganography

Grajeda-Marín, Ismael R.; Montes-Venegas, Hector A.; Marcial‐Romero, J. Raymundo; Hernández-Servín, J. A.; Jiménez, Vianney Muñoz; Luna, Guillermo De Ita

doi:10.1142/s0218001418600108

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…However, we have achieved a higher AEC for the Baboon image than the techniques [12,26]. It also outperforms the six state-of-the-art works [6,8,11,12,20,26] in term of PSNR. Although, the proposed technique has achieved a higher PSNR value for the Baboon image than the techniques [3,19,27].…”

Section: Pixel Difference Histogram (Pdh)mentioning

confidence: 65%

“…The proposed technique outperforms the six state-of-the-art works [3,6,11,19,20,27] in terms of AEC. However, we have achieved a higher AEC for the Baboon image than the techniques [12,26].…”

Section: Pixel Difference Histogram (Pdh)mentioning

confidence: 88%

“…Shen et al [29] have developed a scheme using PVD and EMD techniques. Gradeja et al [6] have developed an optimization technique for resolving the overflow/underflow problem in PVD steganography. Li and He [19] have developed a steganographic technique that combines PVD, modulus function (MF), and particle swarm optimization (PSO) based on earlier research works.…”

Section: Pixel Value Differencing (Pvd) Methodsmentioning

confidence: 99%

“…The proposed two-layer steganographic technique has been compared with the recent stateof-the-art works [3,6,8,11,12,19,20,26,27]. Table 3 displays the embedding capacity and PSNR value of the images Airplane, Baboon, Boats, Lena, and Peppers.…”

Section: Comparative Analysis With the Recent State-of-the-art Workmentioning

confidence: 99%

“…Data hiding [16,17,28,29,39] is a prominent technique for secret communication to hide messages into cover media like text, audio, image, and video. Steganography [6,22,31] is one of the data hiding techniques for embedding the confidential data within the cover media imperceptibly. It is the art of secret communication through cover media in such a way that prevents the detection of hidden data.…”

Section: Introductionmentioning

confidence: 99%

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High capacity data hiding based on multi-directional pixel value differencing and decreased difference expansion

Mandal

Mukherjee

2021

Multimed Tools Appl

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Data hiding is an important research topic over the years. The PVD based techniques embed secret data into the cover image on the difference between the two neighboring pixels. Most of the PVD based techniques are unidirectional that signifies less embedding capacity. The embedding capacity of the PVD technique is utilized in this article to embed the data in multi-directions. To improve the embedding capacity, a new high capacity data hiding technique based on decreased difference expansion (DDE) method and the multidirectional pixel value differencing (MPVD) is proposed. It is based on the block embedding technique for embedding data in the non-sequential locations. The cover image is divided into 2 × 2 non-overlapping blocks. A basic pixel is selected and data hiding takes place in three pixel pairs containing the basic pixel. Distortion due to message embedding is high in the difference expansion (DE) based data hiding techniques. DDE method improves the DE method by reducing the difference between two pixels with a logarithm function and hence increases the quality of the images compared to the DE method. The proposed technique achieves an average embedding capacity of 2.88 bpp which is higher than several recent state-of-the-art works. It withstands the tests of StirMark Benchmark 4.0, and the steganalysis attacks.

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Section: Pixel Difference Histogram (Pdh)mentioning

confidence: 65%

Section: Pixel Difference Histogram (Pdh)mentioning

confidence: 88%

Section: Pixel Value Differencing (Pvd) Methodsmentioning

confidence: 99%

Section: Comparative Analysis With the Recent State-of-the-art Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High capacity data hiding based on multi-directional pixel value differencing and decreased difference expansion

Mandal

Mukherjee

2021

Multimed Tools Appl

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Deep learning based image steganography: A review

Wani

Sultan

2022

WIREs Data Min & Knowl

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A review of the deep learning based image steganography techniques is presented in this paper. For completeness, the recent traditional steganography techniques are also discussed briefly. The three key parameters (security, embedding capacity, and invisibility) for measuring the quality of an image steganographic technique are described. Various steganography techniques, with emphasis on the above three key parameters, are reviewed. The steganography techniques are classified here into three main categories: Traditional, Hybrid, and fully Deep Learning. The hybrid techniques are further divided into three sub‐categories: Cover Generation, Distortion Learning, and Adversarial Embedding. The fully Deep Learning techniques, based on the nature of the input, are further divided into three sub‐categories: GAN Embedding, Embedding Less, and Category Label. The main ideas of the important deep learning based steganography techniques are described. The strong and weak features of these techniques are outlined. The results reported by researchers on benchmark data sets CelebA, Bossbase, PASCAL‐VOC12, CIFAR‐100, ImageNet, and USC‐SIPI are used to evaluate the performance of various steganography techniques. Analysis of the results shows that there is scope for new suitable deep learning architectures that can improve the capacity and invisibility of image steganography. This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning Technologies > Artificial Intelligence

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Toward stronger energy compaction for high capacity dct-based steganography: a region-growing approach

Baziyad

Rabie

Kamel

2020

Multimed Tools Appl

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A New Optimization Strategy for Solving the Fall-Off Boundary Value Problem in Pixel-Value Differencing Steganography

Cited by 14 publications

References 25 publications

High capacity data hiding based on multi-directional pixel value differencing and decreased difference expansion

High capacity data hiding based on multi-directional pixel value differencing and decreased difference expansion

Deep learning based image steganography: A review

Toward stronger energy compaction for high capacity dct-based steganography: a region-growing approach

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