Colour image steganography using XOR multi-bit ...

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

Colour Image Steganography using XOR Multi-Bit Embedding Process P.Mathivanan1

Dr.A. Balaji Ganesh2

1

Assistant Professor, Electronics and Communication Engineering. Velammal Engineering College. Chennai, India [email protected]

2

chance of detection by intruder. The steganography consists of carrier, secret data and a key used for data retrieval and security. The existing methods use various data embedding technique to hide a watermark data over a carrier data such as image, audio and video. In steganography, the two different domains namely spatial and transform are used for data embedding. In spatial domain technique the secret information is directly convolved with the intensity value of cover image using embedding technique such as LSB substation, Pixel value differencing and edge based embedding process. It is one of the simple and efficient steganography processes. The spatial domain technique may be easily be detected by using statistical analysis and it is less robust to compression, cropping, rotation and noise attack. Transform domain technique embed secret information in selective coefficients of the transformed image and making it more robust to attacks such as compression, cropping, rotation and noise attack. Discrete cosine Transform (DCT), Discrete wavelet Transform (DWT), Discrete Fourier Transform (DFT), Contourlet transform and Ridgelet transform are some of the transform domain techniques used in steganography. The specific band of transform coefficient is only affected by embedding process and the remaining coefficients remain intact. So transform domain is efficient than other embedding method. Low payload capacity and high computational capacity are the major drawback of transform domain approach. With reference to drawbacks of both spatial and transform domain the proposed method develop a novel spatial domain steganography process with minimum distortion and increase in payload capacity[1-10].

Abstract-The paper presents a colour image steganography, in which patient diagnose data is appended along with diagnosis information. The confidential information in the form of audio signal convolved with diagnoses data by using multi-bit embedding process. Each represented bit of both ECG and audio samples are convolved with each other and the resultant samples are given to modulo division process to separate quotient and remainder value. The quotient data is then embedded within selective locations of colour image component. Eventually, the reminder value will act as a key to retrieve original information. The performance of proposed steganography process is validated by estimating various quantitative and statistical metrics, such as peak signal to noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM) and signal to noise ratio (SNR). With increase in payload size the result shows better robustness and imperceptibility between cover and stego image. The absorbed SNR value of the extracted signal is absorbed to be 23 dB with minimum signal degradation. Keywords- ECG signal, Audio signal, Image Steganography, modulo division, Human Visual System (HVS), location selection and XOR Coding I.

Professor, Electrical and Electronics Engineering. Velammal Engineering College. Chennai, India [email protected]

INTRODUCTION

Advancement in telemedicine helps in transmitting bio-signal from remote place for faster diagnose and immediate treatment. Remote diagnose using bio-signal is one of the effective method in clinical applications. According to recent survey by World Health Organisation (WHO) about 30% of world population are affected by Cardio Vascular Disease (CVD). Therefore remote diagnose is very much essential for Cardio Vascular Disease to prevent the loss of life and it also helps physician to speed up the diagnose process. Department of Health and Human Service of US government has introduced a policy called Health Insurance Portability and Accountability (HIPPA). The policy provides that any individual at any circumstance can able to transmit his diagnostic information such as name, age, sex and diagnostic report. Steganography helps in transmitting patient data through a secure channel is very much essential and it reduce the

Least significant bit (LSB) is one of the most popular spatial domain embedding process. The selective position of cover image pixel is replaced with a message bit and the bit replacement process in cover image is performed until last message bit. The variation happened in cover image pixel by

978-1-5386-1887-5/17/$31.00 ©2017 IEEE 1980

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

using LSB bit replacement is so minimal and is not visible to naked eye. The pixel modification in stego image can be easily detected by using statistical identification methods. The above limitation of LSB technique is overcome using LSB – matching (LSBM). In LSBM a constant is either added or subtracted with cover image pixel to minimize the chance of detection. On the other hand LSBM-revisited (LSBMR) use two pixels to embed two bits of secret data. Several problems like embedding information into cover data will increase the chance of data extraction by intruder, inefficient embedding algorithm results in detection of stego image by human visual system (HVS) and problem of maintaining image quality, payload capacity, security and computational complexity [5-10]. The listed drawbacks of spatial domain embedding process are addressed in the proposed method of image steganography. Khan Muhammad et al. proposed stego keydirected adaptive LSB substitution method (SKALSB) and multi-level cryptography. The process uses two level encryption algorithm (TLEA) and multi-level encryption algorithm (MLEA) to hide secret data into cover image. In two level encryption algorithm first bit of secret data is embedded into cover image and the embedded pixel is bit shuffled to increase security of the encryption encryption process. Multi-level algorithm XOR 8 bits of secret data with the cover image pixel. The encrypted pixel is separated into four groups, such that eight and first bit will represent first group then second group with seventh and second bit. Similarly all bits in the encrypted data are grouped in to four blocks. Next secret key generated using shuffling process is done to enhance the security of the khan LSB method [14].

high computation complexity with limited security. Hence the proposed steganography method is proposed to improve security, robustness and imperceptibility. Bio-signal from MIT- BIH database and an audio signal with patient data is first pre-processed using a scaling factor. The scaled samples of ECG and audio signal are convolved using XOR encrypted algorithm. The encryption convolves two different data into single encrypt information. To increase the security and robustness of the proposed method the encrypted information is given to the modulo division process. The reminder obtained from modulo division operation will act as key during data extraction process and the selective region of cover image is embedded with quotient data. RGB image is separated into red, green and blue components and each colour image component is individually tested by using proposed process. By using XOR coding the quotient data obtained from modulo division process is embedded into selective location of cover image component. The security, robustness and imperceptibility of the proposed method are achieved by using modulo division process. Performance of the proposed method is measured using metrics such as peak signal to noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM) and signal to noise ratio (SNR). The proposed steganography process is explained in section2. Section 3 discusses about performance metrics used in this process and section 4 discusses about result and discussion. Summary of the proposed steganography is explained in section 5. II. PROPOSED WORK An audio signal in the form of confidential data and an ECG signal from MIT- BIH database are the two different multimedia information that need to be transmitted in this telemedicine application. Novelty of the proposed process is listed to have clear understanding and the process is explained with five different modules. The first module explain about signal pre-processing, where the signals with negative values are scaled using different scaling factors to make it suitable for XOR embedding process. The information signals with non- negative values are more suitable for XOR encryption process. The scaled information samples of audio and ECG signal are convolved by mean of XOR coding process and it is explained in second module. The next module separates resultant information samples into quotient and remainder value by modulo division process. The quotient array is used as watermark data and the remainder array will act as key. The modulo division process will add additional security to our proposed method. Embedding watermark data into cover image is carried out in fourth module.

Bailey and Curran used stego colour cycle (SCC) to embed message in all the three channels of host colour image. To embed a single message bit the process used one channel at a time and the embedding process is carried out in following sequence R, G, B, R, G and B [17]. Gutub develop a LSB replacement method using PIT algorithm to increase payload of the existing LSB method [16]. PIT method has achieved better robustness by embedding message in the data channel by using an indicator channel. Karim et al. proposed a LSB replacement method to embed one bit of secret data per pixel and security of the method is found to be better than the previous methods [17]. H.R kanan and B. Nazeri used genetic algorithm to embedded message bit into selective location of cover image. With reference to above literature on spatial domain steganography are simple and cost effective but fails to achieve security, robustness and imperceptibility of the stego image. On the other hand LSB method with high payload capacity has

1981

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

Scaling

Audio Signal

ECG signal with embedded data

XOR Coding ECG Signal

Scaling Modulo division R

Q

Remainder Value (Key1)

Colour

Red

Green

Quotient Value

Location Selection using Threshold value (Key2)

Blue

Q XOR Coding for data embedding

Stego Image

Blue

Key2

Red Key1 R

Green

Signal Regeneration

Extracted signal

Extracted Audio Signal

XOR Coding for Data

Blue Q’

Extracted Quotient Value

XOR coding

ECG Signal

De-scaling using Scaling factor

Figure 1 represent block diagram of the proposed steganography process

1982

Blue

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

The RGB cover image is separated into red, blue and green components and to identifies the embedding location for specified image component by mean of threshold selection process. The selected location index of image component and watermarked data are embedded by XOR coding process. The watermarked image component is convolved with other image components to obtain stego image. The final module of the proposed process explains about data extraction. The location index along with generated key are used to extract information signal by XOR coding process. The extracted information signal is separated into audio and ECG signal. The proposed steganography process is explained in the block diagram shown in Fig 1.

Where, S a

'

is the scaled ECG signal after '

scaling process and SWECG resultant ECG signal is obtained from XOR coding. C. Modulo Division Process The modulo division process separate each samples of resultant signal by using a constant. Each resultant signal sample is separated into quotient and remainder array with N samples. The quotient array is used as watermark data and the remainder array will act as key. The key generated using modulo division process is essential for signal restoration. The watermark data (quotient array), key (remainder array) and the constant used in modulo division process will double the security of the proposed process. Assume, a sample value with resultant information of 148 and the constant used for modulo division process is 8. The resultant information sample is separated into quotient and remainder values of 18 and 4. The above modulo process is explained using following expression 4.

The scaling process is used to improve security of the proposed process. The scaling factors are essential during data extraction process. The information signals with negative values are scaled using scaling factors. The non-negative information signals can be easily convolved by XOR coding process. The entire scaling process of information signals are explained using expression 1 and 2.

D u Sa

is the audio signal after scaling

process. S ECG

A. Signal Pre-processing

Sa '

'

[Q , R] ( SWECG ' )mod(c )

(1)

(4)

Where, Q,R Quotient and remainder value obtained '

' SWECG

(Q u C )  R

from modulo division SWECG represents resultant information signal and C is constant used for modulo division process. The remainder value is used as key which is generated within the range of 0 to C-1 and the range is purely depends on the constant value. Assume that constant used for modulo process is 8 and the remainder key value will be in the range of 0 to 7.

(2) '

Where S a represent audio signal, S a is the audio signal after scaling process and D represent scaling factor of audio signal. S ECG is the ECG signal '

from MIT BIH database, S ECG is the scaled ECG signal after scaling process and F represent scaling factor of audio signal [2].

D.

The proposed embedding process includes three processing steps. First step involves threshold selection. Next, to identify the selective location in image component. Eventually, embeds watermark data within selective location of cover image component by using XOR coding technique. The colour image Ic is separated into red, green and blue components Icc. Threshold value used for location selection is obtained by calculating mean value of the selective image component Icc. The image components with selective location index and the quotient value obtained from the modulo division process are embedded using XOR coding. The entire XOR embedding process is explained using the expression 5. The location selection process using threshold value will increase robustness and imperceptibility of the cover image. The selective location in cover image component is very much essential during data retrieval. So the selected

B. XOR Coding The non- negative information samples from scaling process are convolved by mean of XOR coding. Assume, an ECG signal as X=X0, X1, X2,...., Xn-1 and audio signal as Y=Y0, Y1, Y2, ... Yn-1 of N samples representing 8 bits. The embedding technique convolves both signals (Y0, X0) and the process is repeated for n-1 bits in each signal sample. The XOR coding process ensures that the ECG signal can absolutely mask audio signal with minimum distortion. Equation 3 represents XOR coding process. The resultant information signal with N samples are given to the modulo division process to generate watermark data and the key used for data extraction.

SWECG '

Sa ' † S ECG '

Data Embedding

(3)

1983

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

location is will act as key to increase the security of the proposed process. ' I CC †Q

' IWC

III. PERFORMANCE METRICS The performance of the proposed steganography process is measured by Peak signal to noise ratio (PSNR). It is the ratio of maximum value in cover image to the mean square of the difference between cover and stego image and it is explained in the following equation (11, 12). Eventually PSNR value reflects the quality of the stego image. High PSNR value reflects the good quality of the stego image. ª max( I c ) º (11) PSNR 10 log10 « » u 100 ¬ MSE ¼

(5)

'

Where IWC is image component with embedded '

data. I CC represents selected location from image component and Q is quotients obtained from modulo division process. Embedded the image component along with other two image components are combined together to generate the stego image. E.

Data Extraction MSE

The process of data extraction starts with separation of stego image into red, blue and green component. The image components with watermarked data and selective location index of image components are essential to extract the watermark data (quotient) and it is explained in the expression 6

Q

' ' IWC † I CC

1 NuM

(6)

M

Sa'

(Q u C )  R

' ' SWECG † S ECG

Sa

S ECG

Sa' D

NCC

2

¦ ¦ ª¬ I C  IW º¼

(12)

n 1m 1

N

¦ ¦ (I

C

u IW )

m 1 n 1 m n

¦ ¦ (I

W

(13)

)2

m 1 n 1

Where Ic represents the cover image, Iw represents the watermarked colour image and N, M denotes size of the image. SSIM

(7)

(2PC PW  const1 ) u (2V CW  const 2 ) (14) ( P 2C  P 2W  const1 ) u (V 2C  V 2W  const 2 )

the statistical Where, PC , PW , V C , V W are parameters calculated for cover and stego image and const1, const2 are used to avoid division by zero exception.

(8) (9)

' S ECG F

M

NCC and SSIM are the two other performance metrics are used to study the similarity between cover and stego image. The stego image quality is usually represented within the range of 0 to 1. The measured metric values are close to 1 reflects stego image with minimum changes and it is explained in the expression (13, 14).

The extracted watermark quotient, secret key and the constant used in modulo division process are essential to retrieve information signal and the process is explained in expression 7. The retrieved information and the ECG signal are used to extract scaled audio signal by means of XOR coding and it is explained in expression 8. The retrieved audio signal is further de-scaled by using respective scaling factor D that is explained in the expression 9. ' SWECG

N

Signal to noise ratio (SNR) is used to measured noise level in an audio signal. The amount of noise added within a stego signal is measured using SNR. It is the ratio between input signals to their difference in noise level which is measured in decibel (dB) and it is shown in the equation (15)

(10)

The extracted data and stego image are analyzed by using different performance metrics such as MSE, PSNR, and NCC to represent difference between cover image and stego image. SNR is used to represent the quality of extracted audio signal with respect to the original audio signal.

SNR

¦S

¦(S

ECG

2 ECG

 SWECG )2

(15)

SWECG represents ECG Signal after embedding process and S ECG represents input

Where

ECG Signal.

1984

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

To evaluate the amount of randomness in an image is essential to perceive image texture. The randomness in stego and cover image is measured by using entropy H and it is used to get important information about embedding strength in a stego image. The expression for calculating image randomness is shown below (16) n

H

 ¦ I ( i ) logI ( i )

TABLE I. Shows PSNR obtained for normal and arrhythmia database P1- PSNR,P2-NCC,P3-SSIM, S-Signal ECG Arrhythmia

(16)

i 0

Energy and power of the stego image is measured using expression (17, 18). The statistical analysis helps in revealing the variation between cover and stego image. E

¦ ¦ I (i , j ) i

P

1 N

ECG Normal

S

P1

P2

P3

S

P1

P2

P3

100

38.2

0.971

0.963

16272

49.6

0.993

0.963

101

38.1

0.971

0.964

16273

49.1

0.992

0.96

102

38.3

0.972

0.965

16539

49.5

0.993

0.965

103

38.2

0.972

0.963

19090

49.3

0.992

0.963

108

38.2

0.972

0.963

19088

49.5

0.993

0.963

111

38.2

0.972

0.963

19140

49.5

0.993

0.963

121

37.9

0.966

0.97

19830

49.4

0.993

0.97

(17)

2

j

¦¦ I (i , j ) i

2

(18)

j

IV. RESULTS AND DISCUSSION Audio signal with patient information and ECG signal are the two multimedia information need to embed into cover image. The process analyse for both ECG Normal and Arrhythmia database from MIT BIH database [1-15]. Audio and ECG signal are convolved using XOR coding and is embedded within cover image. The technique doubles the data hiding capacity with minimum signal distortion. The difference between the ECG signals and resultant signal after XOR coding is shown in the Figure 2. The resultant ECG signal with embedded audio information has minimum distortion and the difference is not visible to naked eye. Performance of the process is measured using quantitative metrics such as peak signal to noise ratio (PSNR), mean square error (MSE), Structural Similarity Index (SSIM) and normalized cross correlation (NCC). The metrics PSNR, NCC and SSIM are calculated for both Arrhythmia and normal database is as shown in the Table I. PSNR is used to represent quality of the stego image, higher the PSNR value better the quality of the image and that reduce the chance of identification by intruder in human visual system (HVS). The closeness between cover and stego image is calculated by using NCC and SSIM metrics. The measured NCC and SSIM values for the proposed method are close to 1 and it is used to represent the minimum difference between cover and stego image. PSNR, NCC and SSIM measured for Arrhythmia and ECG normal database are found to be better in our proposed method.

Fig 2. represent variation between ECG signal with and without embedded data TABLE II. Shows PSNR obtained for different data size in normal and arrhythmia database R- red, B- Blue, G- green Audio

PSNR -Normal-16272

PSNRArrhythmia-101

SNR

Size 126357

R 49.6

G 49.6

B 49.4

R 37.5

G 37.3

B 37.5

(dB) 22.8

101520

50.5

50.5

50.3

38.4

38.8

38.4

23.6

89207

51.1

51.1

50.9

38.9

39.4

39.0

23.3

78398

51.7

51.6

51.4

39.5

40.0

39.5

23.1

71299

52.1

52.1

51.9

39.9

40.4

39.9

22.2

The maximum size of the audio signal that can be embedded within ECG normal and Arrhythmia database is also analysed. The red, blue and green components of cover image is individually analysed and its metrics values are shown in the TABLE II. The PSNR value of the process decreases with increase in data size of the audio

1985

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

signal and the quality of the stego image is better in ECG normal databas

The stego image quality of the process is verified for different conditions. First arrhythmia and normal ECG signal are varied and check the quality of the proposed method. Second the audio signal size is varied and absorbed the metrics such as PSNR, NCC and SNR. Finally cover image used in the process is also varied for the fixed size of ECG and audio signal. The PSNR obtained by varying different cover image is shown in the Table III. The red, green and blue components have slight change in their metric values. The irrespective of the data size, the selected region required for data embedding are same. Hence region with similar array size is affected in each cover image component, which reflects in PSNR value of the proposed process. The quantitative evaluation of the proposed method is also compared with SKALSB, PIT and SCC techniques. In SKA-LSB and other techniques, maximum of 8 KB secret data is embedded into cover image. The PSNR value of 55.89 dB, 49.20 dB and 45.52 dB are absorbed in the SKA-LSB, PIT and SCC technique.

Fig 3 shows PSNR value obtained for ECG normal database by varying data size. The PSNR value gradually decreases with increased in data size of audio signal. The ECG signal size is varied dynamically with the size of audio signal. The quality of audio signal extracted from the steganography is measured using SNR value. SNR value measured for different size of audio signal is also shown in the Table II . High value of SNR represents the extracted audio signal with less noise.

TABLE III. PSNR obtained by varying different cover image R- red, B- Blue, G- green, CI – Cover Image

Fig 3. shows PSNR value obtained for ECG normal database

Fig 4. shows PSNR value obtained for ECG Arrhythmia database

PSNR -Arrhythmia-101

PSNR- Normal-16272

CI

R

B

R

1

37.50

37.26

37.50

49.63

49.63

49.43

2

38.87

39.13

38.95

49.43

49.43

50.07

3

37.70

37.56

37.55

49.64

49.62

49.43

4

37.51

37.54

37.59

49.63

49.63

49.42

5

37.51

37.54

37.60

49.63

49.63

49.44

6

37.47

37.52

37.53

49.63

49.63

49.44

7

36.49

38.89

37.55

49.63

49.83

49.42

8

37.69

37.63

37.54

49.63

49.63

49.44

G

G

B

9

37.54

37.56

37.42

49.63

49.6

49.41

10

37.49

37.56

37.49

49.64

49.63

49.42

11

37.63

37.72

38.20

49.63

49.64

49.46

The proposed method is embed with 1010 KB of secret data and attained a PSNR value of 49.63 dB for ECG normal and 37.50 dB for arrhythmia database. The proposed method performance is found to be better than SKA-LSB, PIT and SCC techniques with increase in payload size with less chance of HVS detection. Statistical analysis of the proposed process is used to reveal more information of the stego process. Histogram analysis, entropy, energy and power are the metrics used for statistical evaluation that helps to indentify the amount of change in stego process. Table IV, describes statistical parametric results measured for cover image and stego image. The metrics measured for stego image has minimal variation with respect to the cover image value.

The quality of stego image is found to be better in ECG normal database than that of arrhythmia database and its difference are shown in the figure 3 and 4. Lower the data size higher the PSNR value for different components of the cover image. NCC value of cover image component is measured by varying different amount of data size and it is absorbed that NCC value is close to 1 and shown in the Table V. So the amount data size variation doesn’t affect the quality of the stego image.

1986

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

TABLE IV. Shows statistical analysis of the stegnography process Statistical Analysis

Cover Image

Stego Image

Entropy

7.3130

7.3125

Power

0.0183

0.0183

Energy

188687537

188939983

value obtained for ECG normal signal is 51 dB and arrhythmia signal is around 37dB. The NCC and SSIM values are close to 1. The colour image steganography is analyzed by varying payload size and different cover image. The metrics obtained for the proposed process is found to be better. The extracted audio signal with insignificant changes is measured using SNR value and it is found to be around 23 dB. The statistical metrics such as histogram analysis, entropy, power and energy value obtained for stego image has insignificant change over cover image. Hence the proposed method is more reliable for telemedicine application to transmit patient information.

The number of pixel in colour intensity level is distributed graphically by plotting histogram. The distribution of pixel in colour image is measured for cover and stego image and the difference between cover image and stego image has minimal variation that is not visible to normal human eye. The pixel distribution of the cover and stego image is shown in the Fig 5(a) and 5(b)

Fig 5.(a) Cover Image and its histogram V.

Reference [1]. Law P (1996b) Health Insurance Portability and Accountability Act of 1996. Public Law 104-191. US. Statut. Large, 1101936–210. [2]. Ibaida A. & Khalil I (2013i) Wavelet-based ECG steganography for protecting patient confidential information in point-of-care systems. IEEE Trans. Biomed. Eng., 603322– 3330.http://doi.org/10.1109/TBME.2013.2264 539. [3]. Edward Jero S., Ramu P Curvelets-based ECG steganography for data security Electronics Letters, Volume 52, Issue 4, 18 February 2016. [4]. Bertino E. Yang Y. Ooi BC. & Deng RH (2005c) Privacy and ownership preserving of outsourced medical data. Proc. - Int. Conf. Data Eng., (Icde): 521–532. http://doi.org/10.1109/ICDE.2005.111 [5]. Ng HS. Sim ML. & Tan CM (2006f) Security issues of wireless sensor networks in healthcare applications. BT Technol. J., 24(2): 138–144. http://doi.org/10.1007/s10550-0060051-8 [6]. Li M. Yu S. Zheng Y. Ren K. & Lou W (2013e) Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption. IEEE Trans [7]. Chen ST. Guo YJ. Huang HN. Kung WM. Tseng KK. & Tu SY (2014h) Hiding patients confidential data in the ECG signal via a transform-domain quantization scheme topical collection on mobile systems. J. Med. Syst., 38. http://doi.org/10.1007/s10916-014-0054-9 [8]. Huang L-C. Tseng L-Y. & Hwang M-S (2013o) A reversible data hiding method by histogram shifting in high quality medical images. J. Syst. Softw., 86(3): 716–727. http://doi.org/http://dx.doi.org/10.1016/j.jss.2 012.11.024 [9]. Edward Jero S. Ramu P. & Ramakrishnan S (2014p) Discrete Wavelet Transform and Singular Value Decomposition Based ECG

Fig 5.(b) Stego Image and its histogram

CONCLUSION

Multi- bit XOR embedding process hides two information signals (audio and ECG) into cover image. The proposed colour image steganography aims in developing a stego process with robust, imperceptible and with high payload capacity. At first the scaled sampled are convolved using XOR coding to reduce size of information and to increase security of the process. The information samples are separated by modulo division process. The security of the process is doubled with remainder array and the constant in modulo division. During information retrieval remainder key and constant are very much essential. Scaling factors and the selected location index will add additional security to the proposed process. The performance analysis of the process is analyzed by using quantitative and statistical analysis. The quantitative metrics such as PSNR and NCC helps in identifying difference between cover and stego image. The average PSNR

1987

International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017)

Steganography for Secured Patient Information Transmission. J. Med. Syst., 38. http://doi.org/10.1007/s10916-014-0132-z [10]. Su Q. Niu Y. Wang G. Jia S. & Yue J (2014w) Color image blind watermarking scheme based on QR decomposition. Signal Processing, 94(1): 219–235. http://doi.org/10.1016/j.sigpro.2013.06.025 [11]. Pan J. & Tompkins WJ (1985ag) A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng., 32(3): 230–236. http://doi.org/10.1109/TBME.1985.325532 [12]. Huynh-Thu Q. & Ghanbari M (2008) Scope of validity of PSNR in image/video quality Electron. Lett. assessment. http://doi.org/10.1049/el:20080522 [13]. Roy WLOCJ (2010af) Verification and Validation in Scientific Computing. Cambridge University Press. [14]. Muhammad, K., Ahmad, J., Rehman, N.U. Naeem Ur Rehman & Zahoor Jan Muhammad Sajjad(2016). CISSKA-LSB: color image steganography using stego keydirected adaptive LSB substitution method Multimed Tools Appl doi:10.1007/s11042016-3383-5 [15]. K. Sathish Shet, A. R. Aswath, M. C. Hanumantharaju , Xiao-Zhi Gao (2016) Design and development of new reconfigurable architectures for LSB/multi-bit image steganography system Multimed Tools Appl doi:10.1007/s11042-016-3736-0 [16]. Gutub AA-A (2010) Pixel indicator technique for RGB image steganography. Journal of Emerging Technologies in Web Intelligence 2:56–64. [17]. Bailey K, Curran K (2006) An evaluation of image based steganography methods. Multimed Tools Appl 30:55–88 [18]. Muhammad K, Ahmad J, Farman H, Jan Z, Sajjad M, Baik SW (2015) A secure method for color image steganography using graylevel modification and multi-level encryption. KSII T Internet Inf 9:1938–1962 [19]. Muhammad K, Ahmad J, Rehman NU, Jan Z, Qureshi RJ (2015) A secure cyclic steganographic technique for color images using randomization. Technical Journal UET Taxila, Pakistan 19(3):57–64 [20]. Khan A, Siddiqa A, Munib S, Malik SA (2014) A recent survey of reversible watermarking techniques. Inform Sci 279:251–272 [21]. Jiang N, Zhao N, Wang L (2015) LSB based quantum image steganography algorithm. Int J Theor Phys, p 1–17 [22]. Jain, M., Kumar, A. & Choudhary, R.C. Brain Inf. (2016). Improved diagonal queue medical image steganography using Chaos

theory, LFSR, and Rabin cryptosystem doi:10.1007/s40708-016-0057-z [23]. Baig, F., Khan, M.F., Beg, S. et al. Nonlinear Dyn (2016) Onion steganography: a novel layering approach 84: 1431. doi:10.1007/s11071-015-2580-5 [24]. Hayat Al-Dmour, Ahmed Al-Ani (2015) A steganography embedding method based on edge identification and XOR codinghttp://dx.doi.org/10.1016/j.eswa.2015.1 0.024 [25]. Djebbar, F., Ayad, B., Meraim, K.A., Hamam, H.: (2012)Comparative study of digital audio steganography techniques. EURASIP J. Audio Speech Music Process. 2012(1), 1–16 [26]. Bhattacharyya, D., Kim, T.H., Dutta, P(2012) A method of data hiding in audio signal. J. Chin. Instit. Eng. 35(5), 523–528 [27]. Nosrati, M., Karimi, R., Hariri, M(2012) Audio steganography: a survey on recent approaches. World Appl. Program. 2(3), 202– 205 [28]. Hamid, N., Yahya, A., Ahmad, R.B., AlQershi, O.M. (2012) Image steganography techniques: an overview. Int. J. Comput. Sci. Secur (IJCSS) 6(3), 68–187 [29]. Stanescu, D., Stratulat, M., Ciubotaru, B., Chiciudean, D., Cioarga, R., Micea, M.V. (2007) Embedding data in video stream using steganography. 4th IEEE International Symposium on Applied Computational Intelligence and Informatics, pp. 241–244 [30]. Fridrich, J., & Long, M. (2000) Steganalysis of LSB encoding in color images. In: Multimedia and Expo, ICME IEEE International Conference, pp. 1279–1282

1988