Steganography in RGB Images Using Adjacent Mean

Chuang, Yun-Hsin; Lin, Bor-Shing; Chen, Yanxiang; Shiu, Hung-Jr

doi:10.1109/access.2021.3132424

Cited by 11 publications

(2 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lu et al [22] use a large-capacity and inverted steganography network in the image hiding process, which carries high confidentiality when reversing the embedding (extraction) process, and using the forward and backward propagation method to get good results. A new strategy was proposed by [23] to dividing the payload capacity to be embedded to the image through three channels in the red, green, and blue (RGB) color image and calculating the three channels' priority probabilities in order to increase the security of embedding and prevent repetitions that reduce imperceptibility, the result in this method was satisfactory [24], [25] presented a method to hide data in a compressed image cover of the JPEG type, which uses the intermediate image format and processed through high-frequency pixels and the adoption of histogram, but this method has a weak result due to the nature of the compressed image, which does not allow embedding more than the permissible limit.…”

Section: Related Workmentioning

confidence: 99%

Improve steganography system using agents software based on statistical and classification technique

Halboos¹,

Al-Bakry²

2023

Bulletin EEI

View full text Add to dashboard Cite

In digital communications, information security is a paramount necessity. In the hiding algorithm, there are three basic parameters: security, capacity, and imperceptibility. Therefore, there are many ways to design the steganography algorithm, such as least significant bit (LSB), discrete wave transformation (DWT), and discrete cosine transform (DCT). The aim of this paper is to improve agent software design based on a steganography system. It proposed an agent system based on a support vector machine (SVM) classifier to hide a secret message in a certain cover image. The common dataset for steganography uses 80% training and 20% testing to get accurate results. Developing an agent system depends on six statistical parameters such as energy, standard deviation, histogram, variance, mean, and entropy. This resulted in features classified by the SVM classifier to predict the best cover image to be nominated for embedding. Worthy results were obtained in terms of imperceptibility, attack, and cover image prediction by statistical issues.

show abstract

Section: Related Workmentioning

confidence: 99%

Improve steganography system using agents software based on statistical and classification technique

Halboos¹,

Al-Bakry²

2023

Bulletin EEI

View full text Add to dashboard Cite

show abstract

“…Image steganography is primarily utilized to reconstruct the original image without distortion in medical, military, and other domains [8]. Image steganography has advantages like maintaining secret information content [9] and hiding transmission with more security [10] and privacy [11].…”

Section: Introductionmentioning

confidence: 99%

An image steganography using improved hyper-chaotic Henon map and fractal Tromino

Nagarajegowda,

Krishnan

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.</span>

show abstract

Compressive Embedding Method for Reversible Steganography Using XOR Approach

Rathor,

Srivastava,

Raj

et al. 2024

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Steganography in RGB Images Using Adjacent Mean

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 11 publications

References 42 publications

Improve steganography system using agents software based on statistical and classification technique

Improve steganography system using agents software based on statistical and classification technique

An image steganography using improved hyper-chaotic Henon map and fractal Tromino

Compressive Embedding Method for Reversible Steganography Using XOR Approach

Contact Info

Product

Resources

About