Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems

Yang, Chung-May; Weng, Chi-Yao; Wang, Shiuh-Jeng; Sun, Hung–Min

doi:10.1109/tifs.2008.926097

Cited by 379 publications

(165 citation statements)

References 17 publications

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“…A value of 12 bit, combined three 4bit MSB of RGB components per pixel, is used during sorting, in this proposed method. In pixel-value differencing (PVD) [3], the difference is calculated in nonoverlapping blocks, with two consecutive pixels. A significant difference value means an edge area.…”

Section: Hiding and Extracting Datamentioning

confidence: 99%

“…However, a simple LSB algorithm is vulnerable to various attacks [1], [2]. Many improved methods in grayscale images have been proposed to increase: the capacity [3], security [4], or imperceptibility [3], [5], [6]. Steganography for color images has also been improved by extending the previous grayscale image based techniques [7]- [9] or adding adaptive selections on red, green, or blue (R-G-B) color components [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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Data Hiding in Spatial Color Images on Smartphones by Adaptive R-G-B LSB Replacement

Lee

2018

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents an adaptive least-significant-bit (LSB) steganography for spatial color images on smartphones. For each red, green, and blue color component, the combinations of All-4bit, One4bit+Two-2bit, and Two-3bit+One-2bit LSB replacements are proposed for content-adaptivity and natural histograms. The high capacity of 8.4bpp with the average peak signal noise ratio (PSNR) 43.7db and fast processing times on smartphones are also demonstrated key words: steganography, adaptive LSB algorithm, spatial color images, data hiding, smartphones

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Section: Hiding and Extracting Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Hiding in Spatial Color Images on Smartphones by Adaptive R-G-B LSB Replacement

Lee

2018

IEICE Trans. Inf. & Syst.

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show abstract

“…In contrast, psychovisual redundancy within an image is not essential for normal visual processing, and thus it is commonly removed by lossy compression techniques, such as quantization techniques used in JPEG [34]. Besides image compression, psychovisual redundancy is also commonly used for data hiding, especially for the least significant bit-plane (LSB) data hiding techniques [35], [36], [37], which encode secret information within these unintelligible redundancies for human visual perception. Fig.…”

Section: Psychovisual Redundancy In Imagementioning

confidence: 99%

Design of image cipher using latin squares

Zhou²,

Noonan

et al. 2014

Information Sciences

167

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In this paper, we introduce a symmetric-key Latin square image cipher (LSIC) for grayscale and color images. Our contributions to the image encryption community include 1) we develop new Latin square image encryption primitives including Latin Square Whitening, Latin Square S-box and Latin Square P-box ; 2) we provide a new way of integrating probabilistic encryption in image encryption by embedding random noise in the least significant image bit-plane; and 3) we construct LSIC with these Latin square image encryption primitives all on one keyed Latin square in a new loom-like substitution-permutation network. Consequently, the proposed LSIC achieve many desired properties of a secure cipher including a large key space, high key sensitivities, uniformly distributed ciphertext, excellent confusion and diffusion properties, semantically secure, and robustness against channel noise. Theoretical analysis show that the LSIC has good resistance to many attack models including brute-force attacks, ciphertext-only attacks, known-plaintext attacks and chosen-plaintext attacks. Experimental analysis under extensive simulation results using the complete USC-SIPI Miscellaneous image dataset demonstrate that LSIC outperforms or reach state of the art suggested by many peer algorithms. All these analysis and results demonstrate that the LSIC is very suitable for digital image encryption. Finally, we open source the LSIC MATLAB code under webpage https://sites.google.com/site/tuftsyuewu/source-code.

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“…A data hiding method imperceptively distorts an image to embed data into the image, and it extracts hidden data from a stego image that conveys hidden data [1,2,3,4,5,6,7,8]. A reversible data hiding method further restores the original image from a stego image [4,5,6,7,8], thus it is applicable to medical and military images [5].…”

Section: Introductionmentioning

confidence: 99%

A location map-free reversible data hiding method for specific area embedding

Ono

Han

Fujiyoshi

et al. 2009

IEICE Electron. Express

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This paper proposes a reversible data hiding method that embeds data into specific areas such as image edges. Though a reversible data hiding method distorts an original image to hide data, it not only extracts hidden data but also restores the original image from the distorted image. The proposed method memorizes only one parameter for data extraction and image restoration, and no location map recording watermarked positions is required. By simple modification to the conventional method that has the above mentioned features, the proposed method has a new function; hiding data to only specific areas.

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Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems

Cited by 379 publications

References 17 publications

Data Hiding in Spatial Color Images on Smartphones by Adaptive R-G-B LSB Replacement

Data Hiding in Spatial Color Images on Smartphones by Adaptive R-G-B LSB Replacement

Design of image cipher using latin squares

A location map-free reversible data hiding method for specific area embedding

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