Analysis of data hiding in R, G and B channels of color image using various number of LSBs

Khan, Sahib; Khan, Tawab; Mahmood, Toqeer; Ahmad, Nasir

doi:10.1109/intech.2016.7845022

“…Steganographic methods embed a secret message into a cover file before transmission. Therefore, only the sender and the recipient know of its existence or understand it, where it is not obviously been seen by a third-party observer [12–14]. The secret message (plain text) is one in which a sender inserts into a cover file.…”

Section: Introductionmentioning

confidence: 99%

Data hiding technique in steganography for information security using number theory

Rehman

¹

,

Saba

²

,

Mahmood

³

et al. 2018

Journal of Information Science

Self Cite

View full text Add to dashboard Cite

In the current era, due to the widespread availability of the Internet, it is extremely easy for people to communicate and share multimedia contents with each other. However, at the same time, secure transfer of personal and copyrighted material has become a critical issue. Consequently, secure means of data transfer are the most urgent need of the time. Steganography is the science and art of protecting the secret data from an unauthorised access. The steganographic approaches conceal secret data into a cover file of type audio, video, text and/or image. The actual challenge in steganography is to achieve high robustness and capacity without bargaining on the imperceptibility of the cover file. In this article, an efficient steganography method is proposed for the transfer of secret data in digital images using number theory. For this purpose, the proposed method represents the cover image using the Fibonacci sequence. The representation of an image in the Fibonacci sequence allows increasing the bit planes from 8-bit to 12-bit planes. The experimental results of the proposed method in comparison with other existing steganographic methods exhibit that our method not only achieves high embedding of secret data but also gives high quality of stego images in terms of peak signal-to-noise ratio (PSNR). Furthermore, the robustness of the technique is also evaluated in the presence of salt and pepper noise attack on the cover images.

show abstract

“…Here, we observe that ( 9) is equivalent to (7) with Θ = A, s = z and y = b. Finally, we briefly mention a theoretical result related to reconstruction.…”

Section: Reconstruction Of the Approximate Signalmentioning

confidence: 70%

“…In the spatial domain based image steganography scheme, secret data is embedded directly into the image by some modification in the values of the image pixels. Some well-known schemes here are listed in [2,[5][6][7][8][9][10][11]. In the transform domain based image steganography scheme, first, the image is transformed into frequency components, and then the secret data is embedded into these components.…”

Section: Introductionmentioning

confidence: 99%

CSIS: Compressed sensing‐based enhanced‐embedding capacity image steganography scheme

Agrawal

¹

,

Ahuja

²

2021

IET Image Processing

View full text Add to dashboard Cite

Image steganography plays a vital role in securing secret data by embedding it in the cover images. Usually, these images are communicated in a compressed format. Existing techniques achieve this but have low embedding capacity. Hence, the goal here is to enhance the embedding capacity while preserving the visual quality of the stego-image. It is also intended to ensure that the scheme is resistant to steganalysis attacks. This paper proposes a compressed sensing image steganography (CSIS) scheme to achieve these goals. In CSIS, the cover image is sparsified block-wise, linear measurements are obtained, and then permissible measurements are selected. Next, the secret data is encrypted, and 2 bits of this encrypted data are embedded into each permissible measurement. For the reconstruction of the stego-image, ADMM and LASSO are used for the resultant optimization problem. Experiments are performed on several standard greyscale images and a colour image. Higher embedding capacity, 1.53 times more compared to the most recent scheme, is achieved. An average of 37.92 dB PSNR value, and average values close to 1 for both the mean SSIM index and the NCC coefficients are obtained, which is considered good. These metrics show that CSIS substantially outperforms existing similar steganography schemes. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…Here, we observe that ( 9) is equivalent to (7) with Θ = A, s = z and y = b. Finaly, we briefly mention a theoretical result related to reconstruction. In [48], it is shown that for sufficiently small constant C (C > 0), the K-sparse signal s of size N can be approximately reconstructed from M measurements y if M ≥ CK (log N ).…”

Section: Reconstruction Of the Approximate Signalmentioning

confidence: 73%

CSIS: compressed sensing-based enhanced-embedding capacity image steganography scheme

Agrawal¹,

Ahuja²

2021

Preprint

0

View full text Add to dashboard Cite

Image steganography plays a vital role in securing secret data by embedding it in the cover images. Usually, these images are communicated in a compressed format. Existing techniques achieve this but have low embedding capacity. Enhancing this capacity causes a deterioration in the visual quality of the stego-image. Hence, our goal here is to enhance the embedding capacity while preserving the visual quality of the stego-image. We also intend to ensure that our scheme is resistant to steganalysis attacks. This paper proposes a Compressed Sensing Image Steganography (CSIS) scheme to achieve our goal while embedding binary data in images. The novelty of our scheme is the combination of three components in attaining the above-listed goals. First, we use compressed sensing to sparsify cover image block-wise, obtain its linear measurements, and then uniquely select permissible measurements. Further, before embedding the secret data, we encrypt it using the Data Encryption Standard (DES) algorithm, and finally, we embed two bits of encrypted data into each permissible measurement. This is the first attempt to rigorously embed more than one bit. Second, we propose a novel data extraction technique, which is lossless and completely recovers our secret data. Third, for the reconstruction of the stego-image, we use the least absolute shrinkage and selection operator (LASSO) for the resultant optimization problem. This has the advantages of fast convergence and easy implementation. This component is also new.We perform experiments on several standard grayscale images and a color image, and evaluate embedding capacity, Peak Signal-to-Noise Ratio (PSNR) value, mean Structural Similarity (SSIM) index, Normalized Cross-Correlation (NCC) coefficients, and entropy. We achieve 1.53 times more embedding capacity as compared to the most recent scheme. We obtain an average of 37.92 dB PSNR value, and average values close to 1 for both the mean SSIM index and the NCC coefficients, which are considered good. Moreover, the entropy of cover images and their corresponding stego-images are nearly the same. These assessment metrics show that CSIS substantially outperforms existing similar steganography schemes. pp. 1-xii i

show abstract

Analysis of data hiding in R, G and B channels of color image using various number of LSBs

Cited by 10 publications

References 11 publications

Data hiding technique in steganography for information security using number theory

Data hiding technique in steganography for information security using number theory

CSIS: Compressed sensing‐based enhanced‐embedding capacity image steganography scheme

CSIS: compressed sensing-based enhanced-embedding capacity image steganography scheme

Contact Info

Product

Resources

About