Steganography Using LSB Substitution and Pixel Value Differencing

2019 ◽  
pp. 108-135
Keyword(s):  
Block Size ◽  
Experimental Results ◽  
Value Differences ◽  
Central Pixel ◽  
Rs Analysis ◽  
Pixel Value ◽  
Addition And Subtraction ◽  
Pixel Value Differencing ◽  
Difference Histogram ◽  
Lsb Substitution

This chapter describes four techniques that use both the principles of LSB substitution and pixel value differencing (PVD). These techniques use 1×3, 2×2, 2×3, and 3×3 size pixel blocks. In a block, LSB substitution is applied on one of the pixels, which is designated as the central pixel. Using the new value of this central pixel, pixel differences are computed with all its neighboring pixels. Based on these pixel value differences, embedding capacity is decided, and embedding is performed by addition and subtraction operations. The experimental results reveal that when the block size increases, the tolerance to RS analysis and pixel difference histogram (PDH) analysis also increases.

scholarly journals High Capacity Image Steganography Using Modified LSB Substitution and PVD against Pixel Difference Histogram Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Gandharba Swain
Keyword(s):  
Boundary Problem ◽  
High Capacity ◽  
Experimental Results ◽  
Least Significant Bit ◽  
Rs Analysis ◽  
Distortion Measure ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Difference Histogram ◽  
Lsb Substitution

The combination of pixel value differencing (PVD) and least significant bit (LSB) substitution gives higher capacity and lesser distortion. However, there are three issues to be taken into account: (i) fall off boundary problem (FOBP), (ii) pixel difference histogram (PDH) analysis, and (iii) RS analysis. This paper proposes a steganography technique in two variants using combination of modified LSB substitution and PVD by taking care of these three issues. The first variant operates on 2 × 3 pixel blocks and the second technique operates on 3 × 3 pixel blocks. In one of the pixels of a block, embedding is performed using modified LSB substitution. Based on the new value of this pixel, difference values with other neighboring pixels are calculated. Using these differences, PVD approach is applied. The edges in multiple directions are exploited, so PDH analysis cannot detect this steganography. The LSB substitution is performed in only one pixel of the block, so RS analysis also cannot detect this steganography. To address the FOBP, suitable equations are used during embedding procedure. The experimental results such as bit rate and distortion measure are satisfactory.

scholarly journals Digital Image Steganography Using Eight-Directional PVD against RS Analysis and PDH Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Gandharba Swain
Keyword(s):  
Least Significant Bit ◽  
Hiding Capacity ◽  
Central Pixel ◽  
Rs Analysis ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Difference Histogram ◽  
Lsb Substitution

The least significant bit (LSB) substitution techniques are detected by RS analysis and the traditional pixel value differencing (PVD) approaches are detected by pixel difference histogram (PDH) analysis. The PVD steganography can escape from PDH analysis by using the edges in multiple directions. This paper proposes a steganography technique by exploiting the edges in eight directions and also using LSB substitution to resist from both RS analysis and PDH analysis. For every 3×3 pixel block the central pixel is embedded with 3 or 4 bits of data by modified LSB substitution technique. Then this new value of the central pixel is utilized to calculate eight difference values with eight neighboring pixels. These eight difference values are used to hide the data. There are two types with regard to two different range tables. Type 1 uses 3 bit modified LSB substitution and range table 1. Type 2 uses 4 bit modified LSB substitution and range table 2. Type 1 and type 2 are also known as variant 1 and variant 2, respectively. Type 1 possesses higher PSNR and type 2 possesses higher hiding capacity.

Steganography Techniques Based on Modulus Function and PVD Against PDH Analysis and FOBP

2019 ◽  
pp. 157-184
Keyword(s):  
Signal To Noise Ratio ◽  
Experimental Results ◽  
Modulus Function ◽  
Evaluation Function ◽  
Embedding Capacity ◽  
Least Significant Bit ◽  
Rs Analysis ◽  
Block Data ◽  
Pixel Value ◽  
Pixel Value Differencing

This chapter proposes two improved steganography techniques by addressing two problems in the existing literature. The first proposed technique is modulus function-based steganography and it addresses pixel difference histogram (PDH) analysis. The modulus function is used to calculate an evaluation function and based on the value of the evaluation function embedding decision is taken. There are two variants of this technique: (1) modulus 9 steganography and (2) modulus 16 steganography. In modulus 9 steganography, the embedding capacity in a pair of pixels is 3 bits, and in modulus 16 steganography the embedding capacity in a pair of pixels is 4 bits. Both the variants possess higher PSNR values. The experimental results prove that the PDH analysis cannot detect this technique. The second proposed technique is based on pixel value differencing with modified least significant bit (MLSB) substitution and it addresses fall off boundary problem (FOBP). This technique operates on 2×2 pixel blocks. In one of the pixels of a block data hiding is performed using MLSB substitution. Based on the new value of this pixel, three difference values with three neighboring pixels are calculated. Using these difference values, PVD approach is applied. Experimental results prove that the PDH analysis and RS analysis is unable to detect this proposed technique. The recorded values of bit rate and peak signal-to-noise ratio are also satisfactory.

scholarly journals Statistical feature-based steganalysis for pixel-value differencing steganography

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wen-Bin Lin ◽  
Tai-Hung Lai ◽  
Ko-Chin Chang
Keyword(s):  
Security Analysis ◽  
Least Significant Bit ◽  
Stego Image ◽  
Statistical Feature ◽  
Accuracy And Precision ◽  
Rs Analysis ◽  
Feature Based ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Difference Histogram

AbstractPixel-value differencing (PVD) steganography is a popular spatial domain technology. Several PVD-based studies have proposed extended PVD steganography methods. The majority of these studies have verified their security against the regular-singular (RS) analysis. However, RS analysis is aimed at the feature of the least significant bit substitution method, which is relatively less significant for PVD steganography. The pixel difference histogram (PDH) is generally utilized to attack PVD steganography. If the embedding capacity is high, then the features on the PDH are evident; otherwise, the features are less obvious. In this paper, we propose a statistical feature-based steganalysis technique for the original PVD steganography. Experimental results demonstrate that, compared with existing steganalysis technique with weighted stego-image (WS) method, the proposed method effectively detects PVD steganography at low embedding ratios, such that there is no need of using the original embedding parameters. Furthermore, the accuracy and precision of the method are better than those of existing PVD steganalysis techniques. Therefore, the proposed method contributes to the security analysis of the original PVD steganography as an alternative to the commonly used RS, PDH and WS attack techniques.

scholarly journals Digital Image Steganography Using LSB Substitution, PVD, and EMD

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Anita Pradhan ◽  
K. Raja Sekhar ◽  
Gandharba Swain
Keyword(s):  
Image Steganography ◽  
Raster Scan ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Difference Histogram ◽  
Pixel Value Difference ◽  
Two Hybrid ◽  
Lsb Substitution

To protect from pixel difference histogram (PDH) analysis and RS analysis, two hybrid image steganography techniques by appropriate combination of LSB substitution, pixel value differencing (PVD), and exploiting modification directions (EMD) have been proposed in this paper. The cover image is traversed in raster scan order and partitioned into blocks. The first technique operates on 2 × 2 pixel blocks and the second technique operates on 3 × 3 pixel blocks. For each block, the average pixel value difference, d, is calculated. If d value is greater than 15, the block is in an edge area, so a combination of LSB substitution and PVD is applied. If d value is less than or equal to 15, the block is in a smooth area, so a combination of LSB substitution and EMD is applied. Each of these two techniques exists in two variants (Type 1 and Type 2) with respect to two different range tables. The hiding capacities and PSNR of both the techniques are found to be improved. The results from experiments prove that PDH analysis and RS analysis cannot detect these proposed techniques.

scholarly journals Data-Hiding Scheme Using Multidirectional Pixel-Value Differencing on Colour Images

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Pyung-Han Kim ◽  
Eun-Jun Yoon ◽  
Kwan-Woo Ryu ◽  
Ki-Hyun Jung
Keyword(s):  
Data Hiding ◽  
Vision System ◽  
Experimental Results ◽  
Human Vision ◽  
Embedding Capacity ◽  
Secret Data ◽  
Colour Image ◽  
Pixel Value ◽  
The Difference ◽  
Pixel Value Differencing

Data hiding is a technique that hides the existence of secret data from malicious attackers. In this paper, we propose a new data-hiding scheme using multidirectional pixel-value differencing, which can embed secret data in two directions or three directions on colour images. The cover colour image is divided into nonoverlapping blocks, and the pixels of each block are decomposed into R, G, and B channels. The pixels of each block perform regrouping, and then the minimum pixel value within each block is selected. The secret data can be embedded into two directions or three directions based on the minimum pixel value by using the difference value for the block. The pixel pairs with the embedded secret data are put separately into two stego images for secret data extraction on receiver sides. In the extraction process, the secret data can be extracted using the difference value of the two stego images. Experimental results show that the proposed scheme has the highest embedding capacity when the secret data are embedded into three directions. Experimental results also show that the proposed scheme has a high embedding capacity while maintaining the degree of distortion that cannot be perceived by human vision system for two directions.

Data Hiding Method Based on Inter-Block Difference in Eight Queens Solutions and LSB Substitution

2014 ◽  
Vol 8 (2) ◽  
pp. 55-68 ◽  
Author(s):  
Vinay Kumar ◽  
Abhishek Bansal ◽  
Sunil Kumar Muttoo
Keyword(s):  
Data Hiding ◽  
Digital Image ◽  
Copyright Protection ◽  
Data Transmission ◽  
File Formats ◽  
Secure Data Transmission ◽  
Ascii Code ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Lsb Substitution

Data hiding is an emerging field of research for secure data transmission over internet, ensuring ownership identification and copyright protection. A couple of techniques have been proposed based on pixel value differencing (PVD) and eight queens' solutions. In this paper, a new data hiding method based on inter-block difference in eight queen's solutions is presented. The result of inter-block difference is XORED with ASCII code of character from the message and the resultant value is embedded in LSB position. The presented approach is more efficient and it provides a more capacity with good imperceptibility. The approach supports different digital image file formats such as bmp, png and tiff.

Statistical Feature–Based Steganalysis for Pixel-Value Differencing Steganography

2021 ◽  
Author(s):  
Wen-Bin Lin ◽  
Tai-Hung Lai ◽  
Ko-Chin Chang
Keyword(s):  
Least Significant Bit ◽  
Stego Image ◽  
Image Steganalysis ◽  
Detection Analysis ◽  
Suitable Parameter ◽  
Feature Based ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Difference Histogram ◽  
Advance Knowledge

Abstract The security and embedding capacity of pixel-value differencing (PVD) steganography is superior to that of least significant bit replacement steganography. Several studies have proposed extended PVD steganography methods that use the original concept of PVD steganography. The majority of the studies have verified their security against regular-singular detection analysis or pixel difference histogram attacks. Weighted stego image steganalysis is the state-of-the-art technology for PVD steganography. This study proposed a suitable parameter for the estimator based on different relative embedding ratios and the size of normal embedding blocks. The experimental results revealed that the proposed technology does not require advance knowledge of the original image. In addition, the proposed method is accurate and precise at any embedding ratio. In the future, this method may be utilized to analyze the security of extended PVD steganography.

A Review on LSB Substitution and PVD Based Image Steganography Techniques

2016 ◽  
Vol 2 (3) ◽  
pp. 712 ◽  
Author(s):  
Aditya Kumar Sahu ◽  
Gandharba Swain
Keyword(s):  
Information And Communication Technologies ◽  
Data Communication ◽  
Communication Technologies ◽  
Least Significant Bit ◽  
Hiding Capacity ◽  
Information And Communication ◽  
Distortion Measure ◽  
Pixel Value ◽  
Pixel Value Differencing ◽  
Lsb Substitution

<p>There has been a tremendous growth in Information and Communication technologies during the last decade. Internet has become the dominant media for data communication. But the secrecy of the data is to be taken care. Steganography is a technique for achieving secrecy for the data communicated in Internet. This paper presents a review of the steganography techniques based on least significant bit (LSB) substitution and pixel value differencing (PVD). The various techniques proposed in the literature are discussed and possible comparison is done along with their respective merits. The comparison parameters considered are, (i) hiding capacity, (ii) distortion measure, (iii) security, and (iv) computational complexity.</p>

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