scispace - formally typeset
Proceedings ArticleDOI

Cover Image Selection Technique for Secured LSB-based Image Steganography

Reads0
Chats0
TLDR
An LSB-based image steganography technique that uses YCbCr color space for the embedding process and the high correlation coefficient between the candidate cover images' skewness and kurtosis and the probability of detection obtained by the stego-images affirms that these properties of the candidatecover images can help in determining the most suitable candidate cover image to be used.
Abstract
Various researches in image steganography focusing on spatial domain specifically in the least Significant Bit (LSB) embedding method had been conducted to improve the embedding capacity while maintaining high imperceptibility. However, these improvements were countered by various statistical attacks. This paper presents an LSB-based image steganography technique that uses YCbCr color space for the embedding process. Also, a new cover selection method to strengthen the proposed embedding algorithm was introduced in this paper. The cover selection mechanism used the skewness and kurtosis of the candidate cover images as factors to determine if a candidate cover image will yield to a high probability of detection or not. The distortions analysis affirms that the stego-images produced by the embedding method obtained acceptable PSNRs and SSIMs, thus, proving that the stego-images are resistant to Human Visual System. Also, the embedding method presented in this paper produced stego-images that were given a low probability of detection of various statistical analysis attacks. Furthermore, the high correlation coefficient between the candidate cover images' skewness and kurtosis and the probability of detection obtained by the stego-images affirms that these properties of the candidate cover images can help in determining the most suitable candidate cover images to be used.

read more

Citations
More filters
Book ChapterDOI

Securing the COVID Patients’ Medical Records Using Encrypted Image Steganography

TL;DR: In this paper , the authors discuss a novel image steganography method that can withstand either statistical tool-based or machine learning (ML)-based steganalysis attacks to ensure secure transmission of the medical images like chest X-rays, CT or MRI reports to the medical experts or hospitals for early diagnosis and fast recovery of the infected persons.
Proceedings ArticleDOI

Image Steganography using Least Significant Bit (LSB) - A Systematic Literature Review

TL;DR: It can be safely claimed that the image steganography quality is significantly improved through LSB via enhanced PSNR (Peak Signal-to-Noise Ratio) and MSE (Mean Square Error), however, the size and secrecy of secret data is the biggest challenge while applying LSB techniques.
Proceedings ArticleDOI

Image Steganography using Least Significant Bit (LSB) - A Systematic Literature Review

TL;DR: A systematic literature review of the state-of-the-art least significant bit (LSB) approaches for image steganography can be found in this paper , where the authors identify 17 different approaches, 3 leading frameworks and 3 leading tools.
Proceedings ArticleDOI

LSB-based Random Embedding Image Steganography Technique Using Modified Collatz Conjecture

TL;DR: In this paper , the Diffie-Hellman key exchange was combined with a modified Collatz conjecture to generate unique random numbers, which are used to identify the unique pixel locations where the secret message will be embedded.
References
More filters
Journal ArticleDOI

Image quality assessment: from error visibility to structural similarity

TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Book ChapterDOI

Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search

TL;DR: Estimation of the full geometric transformation of bag-of-features in the framework of approximate nearest neighbor search is complementary to the weak geometric consistency constraints and allows to further improve the accuracy.
Journal ArticleDOI

Hiding data in images by simple LSB substitution

TL;DR: By applying an optimal pixel adjustment process to the stego-image obtained by the simple LSB substitution method, the image quality of the stega-image can be greatly improved with low extra computational complexity.
Book ChapterDOI

Attacks on Steganographic Systems

TL;DR: In this paper, the authors present both visual and statistical attacks, making use of the ability of humans to clearly discern between noise and visual patterns, and automate statistical attacks which are much easier to automate.
Journal ArticleDOI

Detecting LSB steganography in color, and gray-scale images

TL;DR: In this article, a reliable and accurate method for detecting least significant bit (LSB) nonsequential embedding in digital images is described. But this method relies on the assumption that the secret message length is derived by inspecting the lossless capacity in the LSB and shifted LSB plane.
Related Papers (5)