Information Hiding in Still Image Based on Variable Steganography Technique to Achieve High Imperceptibility

Rashid, Rakan Mohammed; Baker, Baker Khalid; Mohammad, Omar Farook; Ahmed, Falah Y. H.

doi:10.1109/icsgrc53186.2021.9515235

Cited by 1 publication

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A new technique was proposed by [33] using one channel for signal and two for embedding with a predetermined cycle to increase the robustness of the method used, the results of this method showed high payload capacity in addition to imperceptibility, and in return the method lacks a simpler key exchange. The LSB substitution technique presented by [34] which depends on the adaptive state of adding three bits per pixel in one way for the signal and two for embedding in the form of red, green, and blue (RGB) image, here the payload is relatively high for embedding bits in the central places and neglecting the areas that suffer from low contrast which increases its robustness.…”

Section: Related Studymentioning

confidence: 99%

Hiding text using the least significant bit technique to improve cover image in the steganography system

Halboos

Al-Bakry

2022

Bulletin EEI

View full text Add to dashboard Cite

One of the highest priorities in the era of information technology is to achieve an accurate and effective system for hiding security data. One of the goals of steganography is imperceptability to intruder. So this paper work to increase the imperceptibility on image, which has weaknesses in previous studies, as well as to avoid statistical attacks such as chi-square. A method has been proposed that includes calculating the color contrasts in the homogeneous areas of the image and dividing them according to the color contrast and exploiting the data of pixels that have a high impact to embed on the two first and third bits of least significant bit (LSB) to increase the amount of embedded data, impact regions (IR) classify according to selected features extracted in advance by using the support vector machine (SVM) classifier. Work was done on standard images taken from a standard dataset (USC-SIPI) for two types of gray and color images. The results showed the worth of the proposed method through a high peak signal to noise ratio (PSNR) that reached 89.5 dB due to the distribution of data on pixels according to the proposed method

show abstract

Section: Related Studymentioning

confidence: 99%