Image Steganography Using Mid Position Value Technique

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Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000

ScienceDirect

www.elsevier.com/locate/procedia

Procedia Computer Science 132 (2018) 461–468

International Conference on Computational Intelligence and Data Science (ICCIDS 2018)

Image Steganography Using Mid Position Value Technique Srilekha Mukherjeea,* , Subhajit Roya , Goutam Sanyala a

Computer Science & Engineering Department National Institute of Technology Durgapur Durgapur, India

Abstract This paper presents a steganographic approach of concealing the secret data so as to facilitate secure communication. Arnold transformation has been imposed on the chosen cover image in the first stage. This results in the scrambling of the data bits, thereby disrupting the normal pixel orientation. Thereafter, Mid Position Value (MPV) technique is implemented to embed data bits from the secret image within the scrambled cover. Lastly, inverse Arnold transformation is applied on the above image. This results in a descrambling operation, i.e. reverting back the normal orientation. Henceforth the stego is generated. All the experimental results analyze the outcome of the full methodology. For this purpose, several quantitative and qualitative benchmark analysis pertaining to this approach have been made. All the results show that the imperceptibility, i.e. nondetectability of secret data is well maintained. Also the payload is high with negligible distortion in the image quality. © 2018 2018 The The Authors. Published by by Elsevier Elsevier B.V. Ltd. © Authors. Published This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Data Science Science (ICCIDS (ICCIDS 2018). 2018). Keywords: steganography; mid position value (MPV); peak signal to noise ratio (PSNR) ; similarity measure * Corresponding author. E-mail address: [email protected]

1877-0509 © 2018 The Authors. Published by Elsevier B.V.

Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018).

1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/3.0/) Peer-review under responsibility of the scientific committee of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018). 10.1016/j.procs.2018.05.160

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1. Introduction Security issue is a rising alarm around this modern developing globe. In this context cryptography [1] was developed as an encryption [2] technique to secure the contents of the confidential information. But often this is not sufficient and it becomes necessary to keep the very existence hidden. The purpose of steganography [3] is actually fencing the specified concern. In this paper we have proposed a technique to sustain the steganographic target. A scrambling transformation has been used on the cover image in the beginning. This is done with the incorporation of the Arnold Transform [4] on the cover or host. A randomized distribution of the image pixels is generated, which serves as a carrier for the confidential data bits. The proposed Mid Position Value (MPV) methodology facilitates the insertion mechanism of the data bits. MPV technique speculates on the concept of the middle position and its respective integer values of all the dwelling pixels. This concept is used to generate the keys, which guide the insertion pattern. With the execution of the MPV procedure, pixel data bits get embedded in the shuffled form of cover. Lastly, application of inverse Arnold transform gives the required stego-image [5]. This paper is subdivided into several sections. Section 2 contains a brief discussion of some of the related works. Section 3 discloses the main proposed approach with all the necessary algorithms. Section 4 comprises of the experimental results and discussion. Section 5 infers the conclusion along with the future work. 2. Related works Data hiding by LSB [6] is one of the most simple and traditional methods. This approach works by hiding data in the least significant bits (LSB) of the pixels. Significant though it is but still some changes in the image may impair the embedded data. The method of Pixel Value Differencing (PVD) [7], proposed by Wu and Tsai is another effective steganographic approach. It forms pixel blocks from the cover and alters the pixel difference in each pair of blocks for data insertion. Greater the difference more is the alteration made. Based on PVD, another method of tri way of pixel value differencing is proposed by Chang et al. [8]. This new methodology performed better in terms of payload [9] and PSNR computations. Gray Level Modification (GLM) [10], proposed by Potder et al. is another technique which maps data by remodeling the gray level values of the pixels residing in the image. Specifically the gray level values of selected pixels are compared with the secret data bits. Some techniques of combining the PVD and GLM methods with the aim of increasing the payload have also been proposed. One such approach is put forward by Safarpour et al. [11]. Another effective approach is proposed by Ahmad T et al. [12]. Here, the cover at first is split into some blocks. Later based on certain conditions, data bits are masked in the block edges. 3. Proposed work and algorithms This section encircles the proposed approach in image medium. All the steps of embedding and extraction are shown in Fig. 1 (a) and (b) respectively. In the sender side, the cover image is first scrambled by applying Arnold Transformation over it. A chaotic representation of the cover is the resultant output. This scrambled representation serves as a layer of security since the original pixel positions are scuffled before embedding. Bits from the secret image are then inserted in the above transformed image. For this, we incorporate the proposed Mid Position Value (MPV) technique. This methodology uses the concept of the middle position and its respective values for each of the residing pixels. Further, resting on the specified ground, the key values are computed. Finally, private data bits are embedded following a certain fashion on insertion (given in Table 1). Inverse Arnold Transformation gives back the actual stego-image. In the receiving side, all the steps are executed in correct sequence so as to retrieve the masked image. The flow diagrams of the processes are shown next in fig. 1.



Srilekha Mukherjee al. / Procedia Computer Science 132 (2018) 461–468 Srilekha Mukherjee et al. et / Procedia Computer Science 00 (2018) 000–000

(a)

Fig. 1. (a) The process in the Sender’s side (Embedding); (b) The process in the Receiver’s side (Extraction).

3.1. Embedding This stage efficiently masks the confidential data within a seemingly unimportant chosen cover or host.  Input a cover  Apply Arnold Transform on it  Implement Mid Position Value (MPV) technique to embed data  Incorporate inverse Arnold Transform to generate the Stego Mid Position Value (MPV) technique (Sender) a)

Take an input image (say ‘img1’)

b) Trace the pixel positions (say ‘p(i,j)’) of ‘img1’ using eqn. 1

4633

(b)

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(1)

p(i, j)  (i  1) * m  j

c)

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For each ‘p(i,j)’, estimate the number of digits (say ‘total_digits’) in it

d) Calculate the middle digit position (say ‘mid_position’) by the following eqn. 2 (2)

mid _ position  (total _ digits / 2)  1

e)

Capture the residing integer value (say ‘mid_value’) in ‘mid_position’ of ‘p(i,j)’

f)

Obtain key1 from eqn. 3 (3)

key1  p(i, j ) mid _ value

g) If key1 exceeds the total no of pixels (say ‘T’), then (4)

key1  key1%T

h) Obtain a decimal (say ‘4_bit_dec’) with the last_4_bits of the pixel value at p(i,j) (i.e. ‘pix(key)’) i)

Perform

j)

Insertion takes place in p(i,j) according to Table 1

key2  key1%(4 _ bit _ dec _ pix (key))  1

(5)

Table 1. Conditional Table of Insertion/Extraction Strategies. Estimated key1

Estimated key2

Even (key1%2=0)

Even (key2%2=0)

Even (key2%2=0)

Reverse embedding/extraction of two bit secret data Direct two bit complementary embedding/extraction

Odd (key2%2!=0)

Reverse two bit complementary embedding/extraction

Odd (key2%2!=0) Odd (key1%2!=0)

Insertion/Extraction Technique Direct embedding/extraction of two bit secret data

3.2 Extraction This stage extracts the masked data bits from the stego so as to retrieve the hidden image  Input the stego-image  Apply Arnold Transformation on it  Implement Mid Position Value (MPV) receiving technique to extract data bits  Finally, the secret image is retrieved Mid Position Value (MPV) receiving technique a) Take a stego image (say ‘img2’) b) Repeat steps ‘b’ to ‘i’ from Mid Position Value (MPV) technique (Sender) c)

Extraction of bits takes place in p(i,j) according to Table 1

d) All the extracted data bits fill up individual eight bit pixel arrays, generating the inserted secret image



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4. Experimental Results and Discussion

Cover Image

Secret Image

Scrambled Cover Image

Intermediate Stego Image

Stego Image

Fig. 2. The sequential process of the Embedding Phase

4.1 Embedding Capacity Embedding capacity (may also be referred to as payload) [13] is the maximum limit up to which data can be embedded within the carrier with no significant distortion. Table 2. Comparison of Embedding Capacity IMAGE

IMAGE SIZE

PVD

GLM

Ahmad T et al.

Safarpour et al.

MPV

LENA

128x128

**

2048

2493

3906

4096

PEPPER

256x256

**

8192

10007

15500

16384

512x512

50960

32768

40017

58861

65536

128x128

**

2048

2443

3906

4096

256x256

**

8192

9767

15500

16384

512x512 50685 32768 39034 58861 (** For PVD method, all the images that were used, are of size 512x512)

65536

Table 2 portrays the comparative study of payload or embedding capacity. Out of all the methods compared, the MPV approach has the maximum capacity due to its 2 bit insertion strategy. Therefore, this serves to be an advantage in the field of steganography. 4.2 MSE and PSNR Mean squared error, i.e. MSE [14] can be calculated from the eqn. 6, where C is the cover (consisting MxN pixels) and S is its generated stego. M N 1 (6) MSE  [C (ij )  S (ij )]2 (M * N )

 i 1

j 1

Peak Signal to Noise Ratio (PSNR) [15] is estimated from the obtained MSE values in accordance with eqn. 7. PSNR 

10 log 10 255 2  db MSE

(7)

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Table 3. Comparison of PSNR values IMAGE

IMAGE SIZE

PVD

GLM

Sarfarpour et al. 41.02

MPV

30.50

Ahmad T et al. 44.30

LENA

128x128

36.20

PEPPER

256x256 512x512 128x128

35.00 41.79 38.70

33.20 35.50 38.00

46.80 55.00 43.50

40.80 40.05 40.96

44.67 44.88 44.97

256x256 512x512

35.00 40.97

37.20 34.00

47.50 52.50

38.97 39.12

45.66 45.84

44.55

The above Table (Table 3) displays the PSNR values obtained for various methods with respect to various images. The MPV methodology gives moderate PSNR values which signify that the imperceptibility is maintained. For some cases the method proposed by Ahmad T et al. gives better PSNR results than MPV approach. But in most of the cases, MPV outperforms the others. 4.3 Similarity Measure The function used for estimating the similarity measure [16] is computed in eqn. 8, where ‘ci’ is the cover pixels, ‘si’ is the stego pixel, c and s are the mean values of the cover and stego respectively.

r

 (c

i

c )( si  s )

 (c i  c ) 2

 ( si  s ) 2

(8)

Shown next in Figs. 3, 4 and 5 are the comparison of similarity measure for various methods with respect to different images

Fig. 3. Comparison of Similarity Measure w.r.t Lena Image.



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Fig. 4. Comparison of Similarity Measure w.r.t. Baboon Image.

Fig. 5. Comparison of Similarity Measure w.r.t. Pepper Image.

From the above figures, we can clearly see that for all the cases, the values obtained from the MPV technique lies very close to 1, as compared to all others. This proves that the quality of the image is well maintained, i.e. the cover images and their stegos are highly similar. 5. Conclusion and Future Work Steganography facilitates security for several legitimate purposes during communication [17]. In this paper, we have proposed a steganographic approach in image medium which masks the secret data bits that we want to communicate without any third party intervention. Application of Arnold Transform on the host image layers a level of security in the beginning of the procedure itself. The MPV technique follows a conditional strategy while embedding of secret data bits. Thus, the overall security [18] is endorsed. This methodology promotes high embedding capacity of the carrier image [19]. The experimental results affirm that the generated stego is highly imperceptible [20]. Therefore, it does not attract the attention of unwanted sources. In future, this work can be

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extended to accommodate more secret data bits [21] within the carrier medium. Alongside, there must be no chances of introducing any kind of distortion [22] within the generated stego. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]

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