New Channel Selection Criterion for Spatial Domain Steganography

Zhong, Yane; Huang, Fangjun; Zhang, Dong

doi:10.1007/978-3-642-40099-5_1

Cited by 5 publications

(18 citation statements)

References 12 publications

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“…The major task of this method is determination a position of bit or pair of bits in F that altering these bits makes the value of S equal to the value of d. In extraction, d is simple calculated from binary block of a stego-object by (1).…”

Section: A Block Data Hiding Algorithmmentioning

confidence: 99%

“…There are two effective approaches developed to enhance the security of stego-images against detection attacks [1], block based steganography (matrix encoding [2]- [8]) and channel selection approaches [1], [9]. In matrix embedding approach, Hamming code is applied to minimize the number of required modifications to be made to hide secret bits.…”

Section: Introductionmentioning

confidence: 99%

“…For spatial channel selection criterion, several schemes [1], [10] were presented to improve the security of image steganography. In [10], Sabeti et al proposed a complex based LSB matching (CBL), in which a complexity of a pixel is a sum of the differences between the pixel and its eight neighborhoods.…”

Section: Introductionmentioning

confidence: 99%

“…Contemporaneously, a channel selection criterion, which is presented in [1], only considers a texture characteristic of a pixel to select it to use in data hiding. In some image regions, the difference between pixels is large but the value of pixels is small.…”

Section: Introductionmentioning

confidence: 99%

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A novel secure channel selection rule for spatial image steganography

Nguyen

Arch-int

2015

2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE)

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This paper introduces a novel secure channel selection rule for spatial image steganography. In this scheme, there are two factors considered to identify a pixel, which causes less distortion to cover image, to be modified in data hiding. The first one is an average difference between considered pixel and its neighbors. The value of the considered pixel is the second employed factor. Obtained experimental results reported on 10,000 natural images indicate the higher visual quality and security of our new channel selection rule for spatial image steganography when compared with the previous approaches.

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Section: A Block Data Hiding Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel secure channel selection rule for spatial image steganography

Nguyen

Arch-int

2015

2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE)

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“…To resist the statistical steganalysis, Lou and Hu 3 have proposed reversible histogram transformation function-based LSB steganography technique. Security is the major issue in the spatial domain, thus to increase the security some of the channel selection criteria have been proposed by Zhong 4 . In the frequency domain, the information is hidden in the transform coefficients.…”

Section: Introductionmentioning

confidence: 99%

Image Steganography using Hybrid Edge Detector and Ridgelet Transform

Maheswari

Hemanth

2015

DSJ

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Steganography is the art of hiding high sensitive information in digital image, text, video, and audio. In this paper, authors have proposed a frequency domain steganography method operating in the Ridgelet transform. Authors engage the advantage of ridgelet transform, which represents the digital image with straight edges. In the embedding phase, the proposed hybrid edge detector acts as a preprocessing step to obtain the edge image from the cover image, then the edge image is partitioned into several blocks to operate with straight edges and Ridgelet transform is applied to each block. Then, the most significant gradient vectors (or significant edges) are selected to embed the secret data. The proposed method has shown the advantages of imperceptibility of the stego image is increased because the secret data is hidden in the significant gradient vector. Authors employed the hybrid edge detector to obtain the edge image, which increases the embedding capacity. Experimental results demonstrates that peak signal-to-noise (PSNR) ratio of stego image generated by this method versus the cover image is guaranteed to be above 49 dB. PSNR is much higher than that of all data hiding techniques reported in the literature.Keywords: Steganography, data hiding, information security, ridgelet transform, hybrid edge detector MAHeSwARI & HeMANTH : IMAge STegANogRAPHy USINg HyBRID eDge DeTeCToR AND RIDgeLeT TRANSfoRM 215 processing step, the authors force to extract the edge image by sending the cover image to the hybrid edge detector domain. After that, the edge image is partitioned into several blocks in which the ridgelet transform is applied to each block. Then, the most significant gradient vectors are selected to hide the secret data. finally, inverse ridgelet transform is applied to obtain the stego image. rIdgElEt trAnSform 2.1 continuous ridgelet transformThe continuous ridgelet transform was proposed by Candes and Donoho 12 . given a 1-D wavelet transform which indicate the element (.)where ( ,b θ ) are line parameter and a>0 is a scale parameter. The line parameter cos sin u v b θ+ θ = , known as ridgelets. The continuous ridgelet transform (CRT) for given functionIts inverse formula is given by Starck 13 , et al.from eqns (2) and (3), CRT is calculated by applying wavelet transform in the radon transform. The radon domain equation is given by 2 ( , ) ( , ) ( cos sin )where δ is the dirac function. To obtain the RT, 1-D wavelet transform is applied to the radon transform which is given by 1 2 ( , , ) ( , ) ( )where θ is constant and t is variant. Thus, to obtain the fast RT, the fourier domain is introduced. The 2-D-ffT is applied to the input image, then the 1-D inverse ffT is applied to the radon transform. finally, the 1-D wavelet transform is applied to radon transform to obtain the ridgelet coefficients. finite ridgelet transformThe finite ridgelet transform was developed from the finite radon transform as shown in fig 1. The finite radon transform of a real function (C) defined as a 2-D grid 2 p Z is g...

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