Least Significant Bits Of Pixels Research Articles

A Novel LSB Steganography Technique Using Image Segmentation

Steganography is a process to hide data inside a cover file mostly used in media files like image, video, and audio files. Least significant bit (LSB) steganography is a technique where the least significant bits of pixels are used for information hiding. The purpose of using only those bits is to minimize the visual impact of the hidden data on the image file. LSB technique of steganography is one of the most popular forms of steganography available today. As a result, various steganalysis techniques are developed for this steganography technique. One of them is the visual analysis of pixels through pixel modifications to expose hidden data in a visual manner. The proposed method achieves resistance to this attack using an image segmentation model and extracting the most texture-complex areas of an image and hiding information in these specific areas as pseudo-randomized least significant bit replacements. As the outcome of the study, an alternative approach to LSB steganography that results competitively with existing methods is provided. 

Image Steganography in RGB Color Components using Improved LSB Technique Image Pattern Compression using Weighted Principal Components Algorithm

Steganography is age old method employed to send/share secret information or message to/with the recipient. The hidden information is extracted at the recipient end by using some key. With the advent of internet as communication media, steganography has appeared in the form of digital image steganography with wide spread advancements. Hiding information in Least Significant Bit (LSB) of the host image pixels is one of the most popular methods. In the presented work, an improved LSB technique for textual message embedding is discussed. All three color channels space is used to embed the secret code. Simply manipulating only Least Significant Bit of the pixel is not safe for information hiding and vulnerable to attacks. Therefore, the information is embedded in all three color channels i.e. red, green and blue of the host image by ex-oring of the information bit and pixel bit. The secret message is converted into binary sequence and each bit is xored with R-component pixel value of host image. The resultant xor value is used to manipulate the R-, G- and B-component images for binary sequence insertion to get the stego image. The inverse of the process is used to extract the inserted code from the stego image. The PSNR, entropy, standard deviation and variance for host and stego images are used to evaluate the performance of the algorithm. Utilizing the three color channel enhances the hiding area for the host image three folds. The presented work allows three folds space in host image to embed as many textual information.

Crypto Steganography using linear algebraic equation

Demand of information security is increasing day by day with the exponential growth of Internet. The content of message is kept secret in cryptography, where as steganography message is embedded into the cover image. In this paper a system is developed in which cryptography and steganography are used as integrated part along with newly developed enhanced security model. In cryptography the process of encryption is carried out using symmetric block ciphers with linear algebraic equation to encrypt a message [1] and the obtained cipher text is hidden in to the cover image which makes the system highly secured. Least Significant Bit (LSB) technique is used for message hiding which replaces the least significant Bits of pixel selected to the hide the information. A large number of commercial steganographic programs use LSB as the method of choice for message hiding in 24-bit,8bit-color images, and gray scale images. It is observed from the simulation study that both methods together enhance security significantly.

Message Segmentation to Enhance the Security of LSB Image Steganography

Classic Least Significant Bit (LSB) steganography technique is the most used technique to hide secret information in the least significant bit of the pixels in the stego-image. This paper proposed a technique by splitting the secret message into set of segments, that have same length (number of characters), and find the best LSBs of pixels in the stego-image that are matched to each segment. The main goal of this technique is to minimize the number of LSBs that are changed when substituting them with the bits of characters in the secret message. This will lead to decrease the distortion (noise) that is occurred in the pixels of the stego-image and as result increase the immunity of the stego-image against the visual attack. The experiment shows that the proposed technique gives good enhancement to the Classic Least Significant Bit (LSB) technique.

Transforming LSB Substitution for Image-based Steganography in Matching Algorithms

Simple least-significant-bit (LSB) substitution is a method used to embed secret data in least significant bits of pixels in a host image. The LSB approaches typically achieve high capacity. A simple LSB substitution, which hides secret data directly into LSBs, is easily implemented but will result in bad quality of the stego-image. In order to reduce the degradation of the host image after embedding, an LSB substitution scheme was proposed by Wang et al. They proposed a genetic algorithm to search for approximate solutions. Also, Chang et al. proposed a dynamic programming strategy to efficiently obtain a solution. In this paper, we propose a more general model of Wang et al.'s LSB substitution scheme, called as transforming LSB substitution. In order to overcome the problem associated with the two previous approaches of a long running time, a more efficient approach, referred to as the matching approach, is proposed to find a better solution. Some experiments, demonstrations and analyses are shown in this paper to demonstrate our new scheme and approach.

Inverted pattern approach to improve image quality of information hiding by LSB substitution

Capacity and invisibility are two targets of the methods for information hiding. Because these two targets contradict each other, to hide large messages into the cover image and remain invisible is an interesting challenge. The simple least-significant-bit (LSB) substitution approach, which embeds secret messages into the LSB of pixels in cover images, usually embeds huge secret messages. After a large message is embedded, the quality of the stego-image will be significantly degraded. In this paper, a new LSB-based method, called the inverted pattern (IP) LSB substitution approach, is proposed to improve the quality of the stego-image. Each section of secret images is determined to be inverted or not inverted before it is embedded. The decisions are recorded by an IP for the purpose of extracting data and the pattern can be seen as a secret key or an extra data to be re-embedded. The experimental results show that our proposed method runs fast and has better results than that of previous works.

Detecting and restoring the tampered images based on iteration-free fractal compression

In current research, fractal image compression scheme has been used to compress images. In this paper, we apply this technique to refine characteristic values of a specific image and embed the characteristic values into the least significant bits of pixels in the image. Once the picture is damaged by an intruder, the system can detect the tampered blocks and restore them using characteristic values without requiring the original image. From experimental results, we see that the proposed scheme can precisely achieve the detection of a damaged image and improve the performance of image restoration.