LSB matching revisited

Mielikäinen, Jarno

doi:10.1109/lsp.2006.870357

Cited by 849 publications

(439 citation statements)

References 6 publications

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“…Numbers 5 and 1 are again converted using those pixels with their corresponding Base greater than or equal to 2. Thus, 5 is represented as (12) 4,3 . Numbers 1 and 2 have to be embedded in two different ways, as stated in Phase 2.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Two pixels with grayscale values 80 and 101 are excluded, because their corresponding Base values are zero and one, respectively. Pixels with values of 157 and 165 have formed a pair of pixel units to be given to LSBMR [4] for embedding (one in both of them). In this case, two pixels are spared from being changed, yet the message bits are still extractable.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Unlike LSB replacement and LSBM, which deal with the pixel values independently, LSB matching revisited (LSBMR) [4] modifies the LSBM algorithm in such a way that the choice of incrementing or decrementing the pixel value is no longer random. It performs the operation by using a pair of pixels as a unit.…”

mentioning

confidence: 99%

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Double Phase Modular Steganography Using Error Images of Lossy Wavelet Transforms

Afrakhteh¹,

Lee²

2014

IJCCE

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Abstract-An error image of Lossy Wavelet Transform differentiates a typical cover image from its wavelet transformed cover image which is resulted from applying a desired image compression factor (ICF) to the respective cover image. The proposed method uses such error images for finding where and to what extent a target pixel in a cover image must hold secret bits. The method is also able to embed maximal embedding rates on gray-scale cover images, up to four bits per pixel (bpp), preserving a human vision-imperceptible PSNR value and less detectability. The detectability level is shown by Ensemble classifiers using second order SPAM as their input features. Additionally, the method embeds minimal rates, up to one bpp, while being roughly as undetectable as state-of-the-art schemes such as HUGO (highly undetectable stego) and EA (edge adaptive).Index Terms-Error image, JPEG image compression rates, ensemble classifier, information hiding, steganography.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

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Double Phase Modular Steganography Using Error Images of Lossy Wavelet Transforms

Afrakhteh¹,

Lee²

2014

IJCCE

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“…SURVEY There are various data hiding techniques developed in recent years. J. Mielikainen developed a method [4] which is based on pixel pair matching and it uses a pair of pixels as the embedding unit. The LSB of first pixel carries one bit of information and a binary function of the two pixel values carry another bit of information.This method offers same payload (number of bits embedded in the cover image) as LSB matching with fewer changes to cover image.…”

Section: B Human Visual System and Image Steganographymentioning

confidence: 99%

Edge Analysis Based Data Hiding on Digital Images with PVD Technique

P V

2017

IJRASET

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This paper proposes a new improved steganographic technique based on pixel value differencing [14] which is driven by edge recognition technique. The cover image is divided into blocks and rotated first to generate edges [10] in the image and is used as the candidate region to perform data hiding using pixel value differencing. The basic idea of pixel value differencing is a cover image is partitioned into non-overlapping blocks of two consecutive pixels. A difference value is calculated from the values of the two pixels in each block. All possible difference values are classified into a number of ranges. The difference value then is replaced by a new value to embed the value of a sub-stream of the secret message. The number of bits which can be embedded in a pixel pair is decided by the width of the range that the difference value belongs to. The method is designed in such a way that the modification is never out of the range interval. This method provides an easy way to produce a more imperceptible result than those yielded by simple least-significant-bit replacement methods. The embedded secret message can be extracted from the resulting stego-image without referencing the original cover image. Moreover, a pseudorandom mechanism may be used to achieve secrecy protection.

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“…Embedded information (which is usually encrypted) introduces statistical changes in the least significant bits that makes it possible to detect it (Fridrich et al, 2001). However, several approaches such as randomization of the embedding location and reducing the image capacity can be used to difficult the detection of steganographic content using LSB in BMP images (Mielikainen, 2006).…”

Section: Implemented Sanitizersmentioning

confidence: 99%