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

Lee, Haeyoung

doi:10.1587/transinf.2017edl8232

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…The smartphone CPU is rarely equipped with special help processors for decimal numbers, and operates at a smaller frequency for computers. Moreover, smartphone RAM is smaller in size than those found in computers [11] [14].…”

Section: The Hiding In Lightweight Systemsmentioning

confidence: 96%

Lightweight Steganography Technique for Smartphone

Mohammed¹,

Ahmed²

2019

IJCA

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Steganography is an embedded technique that hides the message in a digital medium for the purpose of safely transferring the message. The objective of this paper is to develop and improve lightweight hiding techniques used in the systems of smartphones, by improving the well-known Least Significant Bit (LSB) algorithm by changing the attribute that is related to its work and suggesting some additions in a way that does not affect processing speed or memory usage and increases security. It also provides a large area for hiding a large secrete message with very little impact on the cover relative to the size of the cover used. This paper proposes several additions and modifications to the algorithm, including: using Pseudo Random Number Generator (PRNG) to determine pixels locations in the cover. Coding process was also proposed for the purpose of increasing randomization and provide statistical distribution unrelated to the original message. The LSB hiding process enables the use of less important bits per pixel. In this case, a distribution of hidden bits within one pixel was suggested by different distributions in each hiding process, making data of repeated hiding useless for the attackers. Various measures (MSE, PSNR, BER, and SSIM) were adopted for the purpose of measuring the quality and efficiency of the proposed algorithm through several experiments including cover image sizes in different dimensions with a variety of data for secret messages, whose results were better than other values.

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Section: The Hiding In Lightweight Systemsmentioning

confidence: 96%

Lightweight Steganography Technique for Smartphone

Mohammed¹,

Ahmed²

2019

IJCA

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“…of LSBs (or bits) that would be modified during the embedding process of each technique is shown in the last column of the table. As can be observed in all the aforementioned LSB-based techniques; these were mainly focused on following steganography objectives, i.e., to increase the embedding capacity [8,10,[13][14][15], to enhance visual imperceptibility [13,15,18,23] and to improve security against detectability and hidden data [13,14,20,21]. However, we found tradeoffs among all these objectives because these are correlated to one another.…”

Section: Related Workmentioning

confidence: 97%

“…Usually, in LSB-based methods, the secret data bits are substituted in k-least significant bits of each pixel. In the literature, various types of LSB-based techniques have been proposed [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] targeted at high capacity, visual quality and security.…”

Section: Related Workmentioning

confidence: 99%

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High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution

et al. 2018

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Steganography is the art and practice of communication using hidden messages. The least significant bits (LSB) based method is the well-known type of steganography in the spatial domain. Usually, achieving the larger embedding capacity in LSB-based methods requires a large number of LSB bits modification which indirectly reduces the visual quality of stego-image and increases the risk of steganalysis detection attacks. In this study, we propose a novel steganography method with data mapping strategy which can reduce the number of bits modification per pixel. In the proposed method, four secret data bits are mapped with the four most significant bits of a cover pixel. Furthermore, the only two LSBs of a pixel are modified to indicate the mapping strategy. Experimental results show that the proposed method is able to achieve 3.48% larger embedding capacity while enhancing the visual quality (i.e., peak signal to noise ratio (PSNR) 3.73 dB) and reducing the modification of 0.76 bits per pixel. Moreover, the proposed method provides security against basic Regular and Singular groups (RS) steganalysis and histogram steganalysis detection attacks.

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“…Steganography word consists of two words of Greek origin, ''steganos'' and ''graphien,'' which, when combined, mean ''covered writing'' [1]. Steganography is a method to secure messages during transmission by concealing them within a carrier such as an image, video, text, or audio, which results in stego media [3]. Carriers may also include hiding information in various formats such as codes, DNA, HTML, XML, or executable files (EXE) [1].…”

Section: A Image Steganographymentioning

confidence: 99%

Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning

Mohamed

Hussam

2021

IEEE Access

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The most significant factor to consider during private information transmission through the internet (i.e., insecure channel) is security. So, to keep this data from unauthorized access during transmission, steganography is used. Steganography is the scheme of securing sensitive information by concealing it within carriers such as digital images, videos, audio, text, etc. Current image steganography methods work as follows; it assigns cover image then embeds the secret message within it by pixels' modifications, creating the resultant stego-image. These modifications allow steganalysis algorithms to detect the embedded secret message. So, a coverless data hiding concept is proposed to solve this problem. Coverless does not mean that the secret message will be transmitted without using a cover file, or the cover file can be discarded. Instead, the secret message will be embedded by generating a cover file or a secret message mapping. In this paper, a novel, highly robust coverless image steganography method based on optical mark recognition (OMR) and rule-based machine learning (RBML) is proposed.INDEX TERMS Coverless information hiding, optical mark recognition (OMR), rule-based machine learning (RBML), image steganography.

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

Cited by 4 publications

References 9 publications

Lightweight Steganography Technique for Smartphone

Lightweight Steganography Technique for Smartphone

High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution

Coverless Image Steganography Based on Optical Mark Recognition and Machine Learning

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