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

Authors: Pyung-Han Kim; Eun-Jun Yoon; Kwan-Woo Ryu; Ki-Hyun Jung
Journal: Security and Communication Networks
 (2019)

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## Abstract

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.


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## Body

## 1. Introduction

In recent years, the use of the Internet has become more increased towing to the development of computer performance and communication technology. Therefore, digital contents such as image, video, movie, and audio files are generally used to transmit and receive each other on the Internet. Digital contents have many advantages such as convenience of transmission and ease of use. Therefore, information exchange using digital contents is becoming common. However, digital contents are easy to change and can be duplicated on the Internet that has the characteristics of an open space. Thus, there are many problems that can infringe on the copyright of an individual or an organization. To solve these problems, cryptographic techniques and data-hiding techniques are used to prevent illegal use of information. Cryptography encrypts data with embedded secret data [1, 2]. Cryptography can prevent the transmitted data from being manipulated or leaked. On the other hand, data-hiding techniques hide the existence of secret data [3, 4]. Therefore, malicious attackers cannot know the existence of secret data in digital contents. Data hiding or digital watermarking techniques are used steadily for complete digital information and copyright protection. Watermarking embeds a watermark on digital contents to prevent copyright problems [5, 6]. Data-hiding techniques can be divided into various categories according to classification criteria. Data-hiding methods can be classified into hidden channel technique, steganography technique, anonymity technique, and technique for hiding copyright information. The covert channel technique reduces the signal-to-noise ratio in order to reduce the bandwidth of the base channel, so that the secret data are not exposed to others [7, 8]. Therefore, only authorized senders and receivers can know whether the secret data exist or not. In other words, it is a technique to transmit secret data through a secret path. Steganography is a technique that hides the existence of secret data [9–11]. Steganography can hide the secret data in common digital contents such as image, audio, and video file, so that the secret data to be transmitted cannot be detected by human senses. Conventional cryptography technique sends the encrypted data together with secret data to conceal confidential information, while steganography is related to hide the existence of secret data. Anonymity is a technique that hides the subject of communication and conceals the identity of the sender and the receiver that transmit and receive secret data, where the secret channel between the sender and the receiver is not exposed [12]. Information hiding techniques for hiding copyright information can be categorized into robustness and ductility. Watermarking and fingerprinting are robustness methods. The watermarking technique records copyright information in a video, audio file, or image file for copyright protection. If attackers attempt to modify the digital watermarking information in an illegal manner, the original video or audio files cannot be used. Fingerprinting is a technique that inserts buyer information into contents to track which purchaser has started illegal distribution of contents when illegal distribution of contents occurs [13]. In particular, data-hiding techniques can be classified into data-hiding method and reversible data-hiding method. The data-hiding methods using digital images embed the secret data after changing the pixels of the original cover image. Therefore, the stego image having the embedded secret data is distorted. In order to solve distortion problems, various techniques have been suggested, but such distortions have been acted sensitively in the fields of military, medical, and artwork. As a result, reversible data-hiding methods are being actively researched not only to recover the original cover image but also to extract the secret data [14, 15]. LSB (Least Significant Bit) and PVD (Pixel-Value Differencing) are typical examples of data-hiding techniques. The LSB is a technique to hide secret data into the least significant bits so that it cannot be easily recognized by the human eye [3]. Generally, when secret data are hidden by using up to 3 least significant bits, distortion of the image cannot be perceived by human eyes. However, image distortion can be perceived by human eyes when the least significant bits are used more than 4. In order to overcome the distortion problem, the optimal LSB replacement algorithm has been proposed [16], and Wang et al. proposed an improved scheme using the genetic algorithm [17]. The PVD scheme proposed by Wu and Tsai uses difference values of two consecutive pixels in a block to determine the size of the secret data [18]. Also, an improved PVD scheme using the LSB replacement method for the smooth area of the cover image has been proposed by Wu et al. [19]. Wang et al.’s scheme was proposed to improve the image quality by applying the coefficient function to the PVD scheme [20]. In 2009, Chang et al. proposed a dual image-based data-hiding scheme that could embed secret data into two images [21]. Chang et al.’s scheme improved the function of EMD (Exploiting Modification Direction) scheme which used secret data based on pentadecimal number [22]. However, the embedding capacity of secret data was low in EMD scheme, so Chang et al.’s scheme solved the low embedding capacity problem by using dual image. Lee et al.’s scheme embedded secret data into two images using four directions [23]. Qin et al.’s scheme performed different embedding processes on the two images [24]. The EMD scheme is used for the first image, and three rules are applied to the second image based on the first image to embed the secret data. Lu et al.’s scheme reduced image distortion using CFS (Center Folding Strategy) method, where two stego images using the up and down functions were produced [25]. In 2017, Yao et al. proposed a dual image-based data-hiding scheme using selection strategy of shiftable pixel’s coordinates to improve the scheme proposed by Lu et al. [26]. The technique using PVD on colour image was proposed by Nagaraj et al.’s scheme uses modulus three function with PVD to embed the secret message bits into the colour image [27]. Prema and Manimegalai proposed a technique using three pairs of {(R, G), (G, B), (B, R)} and modified PVD scheme [28]. Swain et al. proposed an adaptive PVD-based colour image-hiding scheme [29]. In 2017, Shiv and Arup proposed a technique to apply PVD scheme to overlapping blocks on colour image. In Shiv and Arup’s scheme, colour image is grouped into two pairs (R, G) and (G, B). The PVD scheme is applied to each pixel pair and performs a reconditioning step to obtain a modified stego image. The concept of redundant blocks is considered to increase embedding capacity. Although there are many techniques to use the PVD on colour image, it still have to improve the image quality and the embedding capacity. In this paper, a novel steganography scheme using the PVD to multidirections on colour images is proposed. We divide a colour image into nonoverlapping blocks and decompose the colour pixels in each block into R, G, and B. The decomposed pixels perform regrouping and find the minimum value to apply the PVD scheme in two directions or three directions. The pairs of pixels on which the PVD scheme was performed are stored in two images and generate two stego images. The remainder of this paper is organized as follows. The pixel-value differencing, data hiding in dual images, and the pixel-value differencing on colour images are explained in Section 2. The proposed scheme is described in Section 3, and the experimental results are analysed in Section 4. Finally, the conclusions are described in Section 5.


## 2. Related Works

In this section, the Wu and Tsai’s PVD scheme and Chang et al.’s dual image-based data-hiding scheme are explained [18, 21]. In addition, we describe the PVD based on the colour image proposed by Shiv and Arup [30].


### 2.1. PVD Scheme

PVD technique determines the size of the secret data that can be hidden by using the difference value of two consecutive pixels in a block. There exists a smooth area and an edge area in images. The edge region is relatively more complicated than the smooth region. When the image is distorted, the change of the smooth area in the human eye can be well distinguished, but the change of the edge area is not well distinguished. Therefore, we can hide more secret data in the edge area than in the smooth area when hiding the secret data in an image. In the PVD scheme, the cover image is separated into blocks, and two consecutive pixels in an each block are used which are defined aspi and pi+1. The pixel values of pi and pi+1 are defined as gi and gi+1, and the difference value di of two pixels is calculated using the following equation:(1)di=gi+1−gi.The difference valuedi ranges from −255 to 255. If di has a value close to 0, it is located in the smooth region, whereas if di has a value close to −255 and 255, it is located in the edge region. When the defined range is Ri and i has a range from 1 to n, li is defined as the lowest value, and ui is defined as the highest value. The range table is defined in Figure 1.Figure 1
Range table.When thei-th block B has the difference value di, the bits can be embedded in the block B are calculated by the following equation:(2)n=log2ui−li+1.The parametern denotes the number of embeddable secret bits. The n binary secret bits are converted into secret data b in decimal. The new difference value di′ is calculated by equation (3) after inserting the secret data.(3)d′=li+b,ford≥0,−li+b,ford<0.The value ofb has a range from 0 to ui−li, so di′ has a range from li to ui. When the value of di′ is calculated, a new pixel value gi′,gi+1′ is calculated from the following equation:(4)gi′,gi+1′=gi+m2,gi+1−m2,ifgi≥gi+1anddi′>di,gi−m2,gi+1+m2,ifgi<gi+1anddi′>di,gi−m2,gi+1+m2,ifgi≥gi+1anddi′≤di,gi+m2,gi+1−m2,ifgi<gi+1anddi′≤di.


### 2.2. PVM Scheme

In 2013, Nagaraj et al. proposed a PVM (Pixel-Value Modification) scheme using a modulus 3 function. The colour cover image is decomposed into R, G, and B, and the modulus 3 operation is performed. The result of the modulus 3 operation is compared with the ternary secret data, and the cover image pixel value is changed according to the compared result. The embedding algorithm is given in Algorithm1.Algorithm 1:Embedding algorithm.
The embedding algorithm of PVM scheme is as follows.
Step 1: Separate RGB colour image into R, G, and B channels
Step 2: Convert decimal secret data to generate ternary secret datad
Step 3: Perform modulus 3 operation on the all pixel values of the R, G, and B grayscale images to generatef
Step 4: Convert the cover image pixel valueci to the stego image pixel value si according to the following conditions.Case 1:f = d, then not modifiedCase 2:f ≠ d and f < d, then si  =  ci + 1Case 3:f ≠ d and f > d, then si  =  ci – 1
Step 5: Merge R, G, and B to generate a stego image.In the extraction process, the stego image is separated into R, G, and B. And the modulus 3 operation is performed on all the pixels to extract the ternary secret data. Finally, the ternary secret data are transformed into the original secret data.


### 2.3. Shiv and Arup’s Scheme

In 2017, Shiv and Arup proposed a PVD scheme based on RGB colour image. RGB colour image is separated into R, G and B grayscale images, and two pairs (R, G) and (G, B) are created for all pixel values for each R, G, and B grayscale images. Then the PVD scheme is applied to generateR1, G1, G2, and B1 for the two pairs. A stego RGB image is generated using the average values of G1 and G2 and the adjusted values of R1 and B1. The embedding algorithm is given in Algorithm 2.Algorithm 2:Embedding algorithm.
The embedding algorithm of Shiv and Arup’s scheme is as follows.
Step 1: Separate RGB colour image into R, G, B channel
Step 2: Create two pairs (R, G) and (G, B)
Step 3: Computet1 and t2 for the threshold: t1=R−G, t2=G−B.
Step 4: Perform Steps 5 through 7. If the threshold is less than the sum oft1 and t2.
Step 5: Apply the PVD scheme to generateR1,G1 and G2,B1 on two pairs
Step 6: Calculate the average value ofG1 and G2 to generate Gs: Gs=G1+G2/2
Step 7: CalculateRs, Bs using Rs=R1−G1−Gs, Bs=B1−G2−Gs
Step 8: Repeat Steps 1 through 7 for all pixelsIn the extraction process, the stego RGB image is separated intoRs, Gs, and Bs. And two pairs Rs,Gs and Gs,Bs are created. Then, the extraction algorithm of PVD scheme is applied to Rs,Gs and Gs,Bs to extract secret data.


## 2.1. PVD Scheme

PVD technique determines the size of the secret data that can be hidden by using the difference value of two consecutive pixels in a block. There exists a smooth area and an edge area in images. The edge region is relatively more complicated than the smooth region. When the image is distorted, the change of the smooth area in the human eye can be well distinguished, but the change of the edge area is not well distinguished. Therefore, we can hide more secret data in the edge area than in the smooth area when hiding the secret data in an image. In the PVD scheme, the cover image is separated into blocks, and two consecutive pixels in an each block are used which are defined aspi and pi+1. The pixel values of pi and pi+1 are defined as gi and gi+1, and the difference value di of two pixels is calculated using the following equation:(1)di=gi+1−gi.The difference valuedi ranges from −255 to 255. If di has a value close to 0, it is located in the smooth region, whereas if di has a value close to −255 and 255, it is located in the edge region. When the defined range is Ri and i has a range from 1 to n, li is defined as the lowest value, and ui is defined as the highest value. The range table is defined in Figure 1.Figure 1
Range table.When thei-th block B has the difference value di, the bits can be embedded in the block B are calculated by the following equation:(2)n=log2ui−li+1.The parametern denotes the number of embeddable secret bits. The n binary secret bits are converted into secret data b in decimal. The new difference value di′ is calculated by equation (3) after inserting the secret data.(3)d′=li+b,ford≥0,−li+b,ford<0.The value ofb has a range from 0 to ui−li, so di′ has a range from li to ui. When the value of di′ is calculated, a new pixel value gi′,gi+1′ is calculated from the following equation:(4)gi′,gi+1′=gi+m2,gi+1−m2,ifgi≥gi+1anddi′>di,gi−m2,gi+1+m2,ifgi<gi+1anddi′>di,gi−m2,gi+1+m2,ifgi≥gi+1anddi′≤di,gi+m2,gi+1−m2,ifgi<gi+1anddi′≤di.


## 2.2. PVM Scheme

In 2013, Nagaraj et al. proposed a PVM (Pixel-Value Modification) scheme using a modulus 3 function. The colour cover image is decomposed into R, G, and B, and the modulus 3 operation is performed. The result of the modulus 3 operation is compared with the ternary secret data, and the cover image pixel value is changed according to the compared result. The embedding algorithm is given in Algorithm1.Algorithm 1:Embedding algorithm.
The embedding algorithm of PVM scheme is as follows.
Step 1: Separate RGB colour image into R, G, and B channels
Step 2: Convert decimal secret data to generate ternary secret datad
Step 3: Perform modulus 3 operation on the all pixel values of the R, G, and B grayscale images to generatef
Step 4: Convert the cover image pixel valueci to the stego image pixel value si according to the following conditions.Case 1:f = d, then not modifiedCase 2:f ≠ d and f < d, then si  =  ci + 1Case 3:f ≠ d and f > d, then si  =  ci – 1
Step 5: Merge R, G, and B to generate a stego image.In the extraction process, the stego image is separated into R, G, and B. And the modulus 3 operation is performed on all the pixels to extract the ternary secret data. Finally, the ternary secret data are transformed into the original secret data.


## 2.3. Shiv and Arup’s Scheme

In 2017, Shiv and Arup proposed a PVD scheme based on RGB colour image. RGB colour image is separated into R, G and B grayscale images, and two pairs (R, G) and (G, B) are created for all pixel values for each R, G, and B grayscale images. Then the PVD scheme is applied to generateR1, G1, G2, and B1 for the two pairs. A stego RGB image is generated using the average values of G1 and G2 and the adjusted values of R1 and B1. The embedding algorithm is given in Algorithm 2.Algorithm 2:Embedding algorithm.
The embedding algorithm of Shiv and Arup’s scheme is as follows.
Step 1: Separate RGB colour image into R, G, B channel
Step 2: Create two pairs (R, G) and (G, B)
Step 3: Computet1 and t2 for the threshold: t1=R−G, t2=G−B.
Step 4: Perform Steps 5 through 7. If the threshold is less than the sum oft1 and t2.
Step 5: Apply the PVD scheme to generateR1,G1 and G2,B1 on two pairs
Step 6: Calculate the average value ofG1 and G2 to generate Gs: Gs=G1+G2/2
Step 7: CalculateRs, Bs using Rs=R1−G1−Gs, Bs=B1−G2−Gs
Step 8: Repeat Steps 1 through 7 for all pixelsIn the extraction process, the stego RGB image is separated intoRs, Gs, and Bs. And two pairs Rs,Gs and Gs,Bs are created. Then, the extraction algorithm of PVD scheme is applied to Rs,Gs and Gs,Bs to extract secret data.


## 3. The Proposed Scheme

In this paper, we divide colour image into nonoverlapping blocks, and pixel values in each block decompose into R, G, and B. In the embedding process, the R, G, and B pixel values of each block are regrouped to apply the PVD scheme in two directions or three directions. In order to embed the secret data, we find the minimum value in the regrouped blocks. The secret data are embedded in two directions or three directions based on the minimum value. We split two pixels of each pair into two images for the perfect extraction of the secret data. In the extraction process, we extract the secret data by applying extraction algorithm of PVD scheme in two stego images (Algorithms3 and 4).Algorithm 3:The proposed embedding algorithm.
The embedding algorithm is as follows.
Step 1: Divide the colour imageC into 2 × 2 size blocks. If the i-th block is Ci, then the pixels in Ci are defined as: Cj,ki0≤j,k≤1.
Step 2: Decompose the colour pixels in each block into R, G, and B.Cj,ki is decomposed into Rj,ki, Gj,ki, and Bj,ki0≤j,k≤1 by the following equation: C0,0i=R0,0i,G0,0i,B0,0i, C0,1i=R0,1i,G0,1i,B0,1i, C0,2i=R0,2i,G0,2i,B0,2i, C0,3i=R0,3i,G0,3i,B0,3i
Step 3: Regroup theRj,ki, Gj,ki, and Bj,ki pixel values in the block Ci to generate NRj,ki, NGj,ki, and NBj,ki0≤j,k≤1. NRj,ki=NR0,0i=R0,0i,NR0,1i=R0,1i,NR1,0i=R0,2i,NR1,1i=R0,3i, NGj,ki=NG0,0i=G0,0i,NG0,1i=G0,1i,NG1,0i=G0,2i,NG1,1i=G0,3i, NBj,ki=NB0,0i=B0,0i,NB0,1i=B0,1i,NB1,0i=B0,2i,NB1,1i=B0,3i
Step 4: Find the minimum pixel value ofNRj,ki, NGj,ki and NBj,ki, respectively. The min function returns the minimum value. minNRj,ki=minNR0,0i,NR0,1i,NR1,0i,NR1,1i, minNGj,ki=minNG0,0i,NG0,1i,NG1,0i,NG1,1i, minNBj,ki=minNB0,0i,NB0,1i,NB1,0i,NB1,1i.
Step 5: Generate pairs in two directions or three directions.Step 5-1: Construct two pairs in two directions based on the minimum value as shown Figure2. The same algorithm is performed on NRj,ki, NGj,ki and NBj,ki. Therefore, we will explain only NRj,ki. If the minimum pixel value in NRj,ki is minNR0,1i, then the two pairs are minNR0,1i,NR0,0i and minNR0,1i,NR1,1i.Step 5-2: Construct three pairs in three directions based on the minimum value as shown Figure3. The same algorithm is performed on NRj,ki, NGj,ki, and NBj,ki. Therefore, we will explain only NRj,ki. If the minimum pixel value in NRj,ki is minNR0,1i, then the three pairs are minNR0,1i,NR0,0i, minNR0,1i,NR1,0i, and minNR0,1i,NR1,1i.
Step 6: Apply PVD scheme in two directions or three directions.Step 6-1: Perform the PVD scheme to two pairs for embeds the secret data. The two pairs after the PVD scheme is performed which are defined asS1NR0,1i,SNR0,0i and S2NR0,1i,SNR1,1i.Step 6-2: Perform the PVD scheme to three pairs for embeds the secret data. The three pairs after the PVD scheme is performed which are defined asS1NR0,1i,SNR0,0i, S2NR0,1i,SNR1,0i, and S3NR0,1i,SNR1,1i.
Step 7: Distribute pixels in two pairs or three pairs to two imagesStep 7-1: Distribute pixels in two pairs to two imagesRI1 and RI2 as shown Figure 4.Step 7-2: Distribute pixels in three pairs to two imagesRI1 and RI2 as shown Figure 5.
In this step, generatedRI1 and RI2 associated with R channel. GI1 and GI2 are generated in the G channel, and BI1 and BI2 are generated in the B channel.
Step 8: Generates two stego colour imagesS1C and S2C by using the following equation. The merge function combines R, G, and B to produce a colour image. StC=mergeRIi,GIi,BIi,for1≤t≤2.
Step 9: Repeat the above steps for all blocks.Algorithm 4:The proposed extraction algorithm.
In the extraction process, we split the two stego colour images into nonoverlapping blocks and decompose each colour image into R, G, and B. Then, the PVD extraction algorithm is applied to R, G, and B to extract secret data. The extraction algorithm is as follows.
Step 1: Divide the two stego colour imagesS1C and S2C into 2 × 2 size blocks. If the i-th block is StCi0≤t≤1, then the pixels in StCi are defined as StCj,ki0≤j,k≤1.
Step 2: Decompose two stego colour imagesS1C and S2C into R, G, and B. StC is decomposed into RIt, GIt, and BIt.
Step 3: Extract the secret data by using the following equation.pvdEA means extraction algorithm of PVD scheme. secret data=pvdEARI1,RI2‖pvdEAGI1,GI2‖pvdEABI1,BI2.
Step 4: Repeat the above Steps for all blocks.Figure 2
Two pairs in two directions. (a) Case 1: minimum value position (0, 0). (b) Case 2: minimum value position (0, 1). (c) Case 3: minimum value position (1, 0). (d) Case 1: minimum value position (1, 1).
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(d)Figure 3
Three pairs in three directions. (a) Case 1: minimum value position (0, 0). (b) Case 2: minimum value position (0, 1). (c) Case 3: minimum value position (1, 0). (d) Case 1: minimum value position (1, 1).
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(d)Figure 4
Distributed pixels in two pairs.Figure 5
Distributed pixels in three pairs.


## 4. Experimental Results

In this section, we analysePSNR, quality index, and embedding capacity for performance evaluation in the proposed scheme. In the data hiding scheme, PSNR is measuring the degree of distortion between the cover image and the stego image by using the following equation:(5)PSNR=10×log102552MSE.The mean square error is calculated using the following equation:(6)MSE=∑i=1W×Hpi−pi′2W×H.When the size of the image is given asM×N, the cover image is I and the stego image is I′. If the PSNR value is more than 30 dB, the image distortion cannot be detected by human eyes. The quality index is an indicator of the correlation between two images. If quality index is 1, the two images are the same. Conversely, if the quality index is −1, the two images are different images. The quality index is shown in the following equation:(7)Q=4δxypx2¯px2¯δx2+δy2px2¯+py2¯.The equations for each element of equation (8) are as follows:(8)px¯=1wh∑i=0wh−1pi,py¯=1wh∑i=0wh−1pi′,δx2=1wh−1∑i=0wh−1pi−px¯2,δy2=1wh−1∑i=0wh−1pi′−py¯2,δxy=1wh−1∑i=0wh−1pi−px¯−pi′−py¯.The quality index is defined as the combination of loss of correlation, luminance distortion, and contrast distortion which is redefined as the following equation:(9)Q=δxyδxδy⋅2px2¯py2¯px2¯+py2¯⋅2δxδyδx2+δy2.The correlation coefficient between the two images isδxy/δxδy. The luminance between the two images measures by using 2px2¯py2¯/px2¯+py2¯, and the similarity of the two images measures by using 2δxδy/δx2+δy2. The embedding capacity means the size of the secret data that can be embedded into the cover image. Figure 6 shows the 512 × 512 sized colour images used in the experiment.Figure 6
Cover images. (a) Lena. (b) Tiffany. (c) Macaw. (d) Beach. (e) Pens. (f) Girl. (g) Cat. (h) Flower.
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(h)Figure7 shows RI1, RI2, GI1, GI2, BI1, BI2, S1C, and S2C for Lena image after performing the PVD scheme in two directions. RI1 and RI2 are related to R channel, GI1 and GI2 are related to G channel, and BI1 and BI2 are related to B channel. The first stego image is S1C and the second stego image is S2C. Figure 8 shows RI1, RI2, GI1, GI2, BI1, BI2, S1C, and S2C for Lena image after to perform the PVD scheme in three directions. Table 1 compares the PSNR of the proposed scheme with other schemes. The proposed scheme generates two stego images. The PSNR value of the first stego image is PSNR-1 and the PSNR value of the second stego image is PSNR-2. In the case of embedding secret data in two directions, the each PSNR value of the proposed scheme is similar to Shiv and Arup’s scheme or about 1.5 dB lower. And the PSNR value of the proposed scheme is about 10 dB lower than Wu and Tsai’s scheme and about 8 dB lower than Nagaraj et al.’s scheme. However, the proposed scheme keeps the PSNR value above 30 dB on average, so it cannot detect image distortion by human eyes. Table 2 compares the embedding capacity of the proposed scheme and other schemes. The embedding capacity of the proposed scheme is about 2,400,000 bits higher than Wu and Tsai’s scheme and about 1,500,000 bits higher than Nagaraj el al.’s scheme. Also, the embedding capacity of the proposed scheme is about 1,350,000 bits higher than Shiv and Arup’s scheme and about 1,330,000 bits higher than Prema and Manimegalai’s scheme. As a result, the proposed scheme maintains the PSNR of 30 dB or more while the embedding capacity of the secret data is much higher than other schemes. Table 3 compares the quality index of the proposed scheme and Shiv and Arup’s scheme. The quality index of the proposed scheme is similar with Shiv and Arup’s scheme. Table 4 shows the experimental results for each case where secret data are embedded in two directions and three directions in the proposed scheme. When embedding secret data in three directions, the PSNR value is about 2 dB lower in both stego images, but the embedding capacity is approximately 1,800,000 bits higher.Figure 7
Two directions embedding (Lena image). (a) RI1. (b) RI2. (c) GI1. (d) GI2. (e) BI1. (f) BI2. (g) SI1. (h) SI2.
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(h)Figure 8
Three directions embedding (Lena image). (a) RI1. (b) RI2. (c) GI1. (d) GI2. (e) BI1. (f) BI2. (g) SI1. (h) SI2.
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(h)Table 1
Comparison ofPSNR for the proposed scheme and other schemes.
Wu and Tsai’s scheme [18]
Nagaraj et al.’s scheme [27]
Prema and Manimegalai’s scheme [28]
Shiv and Arup’s scheme [30]
Proposed scheme (two directions)
Colour image
Colour image
Colour image
Colour image
S
1
C
S
2
C
Average
PSNR (dB)
PSNR (dB)
PSNR (dB)
PSNR (dB)
PSNR-1 (dB)
PSNR-2 (dB)
Lena
40.0379
49.8948
43.7599
30.7933
30.9791
32.2565
30.9791
Tiffany
41.6758
49.9241
43.8679
31.2709
30.2070
30.4749
30.3409
Macaw
41.4435
50.0031
43.8326
30.4724
30.5083
32.7829
31.6456
Beach
43.1482
49.9013
42.1780
41.0727
35.1747
36.2268
35.7007
Pens
41.8196
49.8866
43.1339
32.6592
29.8021
32.4704
31.1362
Girl
41.7187
49.8949
43.0240
32.8201
30.2782
32.9632
31.6207
Cat
42.5678
49.8925
43.7878
37.4366
34.0405
36.0262
35.0333
Flower
42.0581
49.8871
43.2004
31.2419
30.3256
32.8820
31.6038
Average
41.8087
49.9105
43.3480
33.4708
31.4144
33.2603
32.3373Table 2
Comparison of embedding capacity of the proposed scheme and other schemes.
Wu and Tsai’s scheme [18]
Nagaraj el al.’s scheme [27]
Prema and Manimegalai’s scheme [28]
Shiv and Arup’s scheme [30]
Proposed scheme (two directions)
Capacity (bits)
Capacity (bits)
Capacity (bits)
Capacity (bits)
Capacity (bits)
Lena
1,248,890
524,457
2,305,342
2,516,141
3,739,287
Tiffany
1,225,240
523,641
2,306,394
2,330,381
3,678,830
Macaw
1,211,869
524,261
2,328,347
2,185,173
3,643,723
Beach
1,188,477
523,896
2,305,296
1,588,798
3,597,199
Pens
1,208,497
524,380
2,307,066
1,878,029
3,643,788
Girl
1,234,315
524,351
2,307,919
1,919,833
3,670,028
Cat
1,193,828
524,330
2,305,296
1,700,834
3,591,096
Flower
1,206,025
524,733
2,305,305
2,255,479
3,621,236
Average
1,214,643
524,256
2,308,871
2,046,834
3,648,148Table 3
Comparison of Quality index values of the proposed scheme and Shiv and Arup’s scheme.
Shiv and Arup’s scheme [30]
Proposed scheme
Quality index
Two directions
Three directions
Quality index-1
Quality index-2
Quality index-1
Quality index-2
Lena
0.6870
0.7653
0.7325
0.7735
0.7148
Tiffany
0.4686
0.4181
0.4563
0.3767
0.4863
Macaw
0.4486
0.4456
0.4401
0.4477
0.4348
Beach
0.3297
0.3346
0.3284
0.3451
0.3190
Pens
0.2370
0.2248
0.2354
0.2221
0.2362
Girl
0.0712
0.0719
0.0693
0.0733
0.0677
Cat
0.3792
0.3804
0.3755
0.3802
0.3756
Flower
0.3294
0.3415
0.3344
0.3456
0.3300
Average
0.3688
0.3727
0.3714
0.3705
0.3705Table 4
Comparison of experimental results of two-way embedding and three-way embedding.
Images
Proposed scheme
Two directions
Three directions
PSNR-1 (dB)
PSNR-2 (dB)
Capacity (bits)
PSNR-1 (dB)
PSNR-2 (dB)
Capacity (bits)
Lena
30.9791
32.2565
3,739,287
26.9258
29.5024
5,671,307
Tiffany
30.2070
30.4749
3,678,830
27.6425
30.8193
5,557,627
Macaw
30.5083
32.7829
3,643,723
28.0548
30.2345
5,507,035
Beach
35.1747
36.2268
3,597,199
32.1241
35.2979
5,420,140
Pens
29.8021
32.4704
3,643,788
28.2435
30.0250
5,563,465
Girl
30.2782
32.9632
3,670,028
28.7210
30.4778
5,601,851
Cat
34.0405
36.0262
3,591,096
33.2151
35.9475
5,429,264
Flower
30.3256
32.8820
3,621,236
29.1141
31.1358
5,494,673
Average
31.4144
33.2603
3,648,148
29.2551
31.6800
5,530,670Figures9 and 10 show histograms about frequency of image pixel values. In the histogram, the leftmost value means 0 and the rightmost value is 255. Pixels with a high frequency in the image have a high shape. As shown in Figures 9 and 10, the shape of the histogram changes when the secret data is embedded.Figure 9
(a) Flower cover image (b–d) R, G, B histograms of cover image (e) Flower stego image (f–h) R, G, and B histograms of Shiv and Arup’s scheme.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)Figure 10
(a) Flower cover image. (b–d) R, G, and B histograms of cover image. (e) Flower stego imageS1C. (f–h) R, G, and B histograms of the proposed scheme on S1C. (i) Flower stego image S2C. (j–l) R, G, and B histograms of the proposed scheme on S2C.
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)


## 5. Conclusion

In this paper, a data-hiding scheme using multidirectional pixel-value differencing based on colour image has been proposed. The colour image was divided into nonoverlapping sub-blocks and then decomposed with three channels. The minimum value was determined in the each block, and the pixel-value differencing scheme was applied in two or three directions based on the minimum value. Two or three pairs with the secret data were stored separately in two grayscale images. The proposed embedding method was performed on R, G, and B channels and combined the grayscale images into two colour stego images. The colour stego images were separated into R, G, and B channels, and the secret data were extracted using the pixel-value differencing on two or three directions. The experimental results demonstrated that the proposed scheme had a high embedding capacity and acceptable imperceptibility in the visual image quality. The proposed method could hide 1,601,314 bits more than previous method. In the future, new data-hiding schemes will be worked to increase the embedding capacity by combining the pixel-value differencing scheme and secret sharing scheme.



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*Source: 9038650-2019-10-31.xml*