QR code based color image cryptography for the secured transmission of ECG signal P. Mathivanan & A. Balaji Ganesh Multimedia Tools and Applications An International Journal ISSN 1380-7501 Multimed Tools Appl DOI 10.1007/s11042-018-6471-x 1 23 Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media, LLC, part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to selfarchive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”. 1 23 Author's personal copy Multimedia Tools and Applications https://doi.org/10.1007/s11042-018-6471-x QR code based color image cryptography for the secured transmission of ECG signal P. Mathivanan 1 & A. Balaji Ganesh 2 Received: 13 October 2017 / Revised: 26 March 2018 / Accepted: 25 July 2018 # Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract The paper presents a novel QR code based color image steganography to facilitate remote transmission of ECG signal along with patient diagnosis data. The proposed system is configured with two stage security processes, namely pixel permutation and chaos encryption. A three step sequential strategy is adapted for the implementation of the system. At first, the diagnose information in the form of alphanumeric data is given to QR code generator for the generation of equivalent 2D binary matrix. Similarly, the ECG signal samples are transformed into alphanumeric cipher text by involving integer to binary sequence conversion process and eventually into corresponding QR codes. Secondly, the both diagnose data as well as ECG signal integrated QR codes are embedded within individual R, G and B color image components by using the pixel permutation process. Finally, the color image components, R, B and G are individually encrypted by using 1D chaotic encryption technique as an attempt to enhance further the security of existing steganography process. The experimental results are validated in terms of NCPR, UACI and also entropy which found to be better and comparable than other baseline algorithms. Keywords Pixel permutation . Color image encryption . Chaos . Security analysis 1 Introduction The remote transmission of confidential and sensitive data over communication channels is still considered as a challenging task [3, 7, 20]. The data security and preserve of confidentiality have become tremendous pressurized job in all other applications [1, 32, 36, 46], among others medical diagnosis reports and imaging are considered very important. The recent advancements in communication have shown the great opportunities for preserving and forwarding the large * A. Balaji Ganesh abganesh@velammal.edu.in 1 Department of Electronics and communication Engineering, Velammal Engineering College, Chennai 600066, India 2 Electronic System Design Laboratory, Department of Electrical and Electronics Engineering, Velammal Engineering College, Chennai 600066, India Author's personal copy Multimedia Tools and Applications volumes of digital contents across the globe in a minimum of time [9, 12, 17, 21, 24, 29, 49]. At the same time, safe guarding of original contents from intruders or unauthorized person are considered very essential. In general, the image and text encryptions are differed from each other due to various intrinsic features, such as data capacity, high redundancy and strong correlation among pixels. The statistical analysis submitted by US national security agency states that the primary mode of secured information exchange is mainly done in the form of image (cover data) [19, 34]. Steganography and cryptography are widely used to preserve confidentiality, integrity and availability of secret data to the authorized users. The confidentiality assures data privacy, it means only authorised user can access transmitted data. Integrity represents data assurance that the information has not been modified during transmission. To assure accessibility of confidential data to an authorized user at any time is represented as availability. The wide area network is an open ended architecture and can be hacked by any persistent intruder. The process of manipulating and altering cover image pixel in order to hide confidential original data is known as steganography. The direct manipulation of cover image pixel is performed by involving various conventional spatial domain techniques, such as Least significant bit (LSB), pixel value difference (PVD) and histogram based steganography process. The cover image pixel values are transformed into coefficient values and embedded secret information into specific locations by means of transform domain technique. Discrete cosine transforms (DCT), discrete wavelet transform (DWT) and discrete Fourier transform (DFT) are widely used transform domain techniques. Cryptography is a process of transforming meaningful information into an authorized user in readable format, for example cipher text. It is understood that many conventional encryption algorithms, including International data encryption algorithm (IDEA), data encryption standard (DES), and Rivest, Shamir and Adleman algorithm (RSA) are found to be producing poor results in image encryption applications. In contrast, chaos based cryptographic technique is found to be more efficient [16]. In general, chaotic systems are known for its non-periodic characteristics, high sensitiveness to initial condition and random in nature, that makes the image encryption technique more robust against statistical attack [16]. Muhammad Bilal et al. have presented a chaos based zero steganography algorithm to hide the payload on the basis of certain relationship between cover image and chaos matrix. The entire payload embedding and retrieval process have not modified the original cover image, hence it has been claimed as zero steganography technique [8]. Mamta Jaina et al. have introduced an adaptive circular queue image steganography with RSA cryptosystem which is employed, dynamically to embed secret cipher blocks in the form of circular queues. Further, the RSA cryptosystem has ensured better security, privacy and confidentiality of secret information [14]. Sabry S. Nassar et al. have proposed a secured wireless image communication mechanism by involving the combined features of LSB steganography and chaotic baker map. In which, the LSB steganography technique is used to conceal confidential information within a cover image whereas 2D chaotic baker map has been adapted to increase the security as well as privacy of confidential information [28]. Mamta Jain et al. have proposed an enhanced diagonal queue medical image steganography process by combining chaos theory, linear feedback shift register (LFSR), and Rabin cryptosystem. The work employs linear feedback shift register for the generation of pseudo random sequence which is followed by transmission of confidential medical data transmission in the form of standard chaos map. Further security is ensured through Rabin cryptosystem [15]. Ratnakirti Roy et al. have introduced a chaos based edge adaptive image steganography technique which is based on embedding of secret data into randomly chosen region of interest (ROI) and it is followed by Author's personal copy Multimedia Tools and Applications chaotic map process to ensure better improved security [35]. A color image steganography process has been suggested along with heterogeneous bit permutation as well as correlated chaos and results showed better reduction in computational cost but improved efficiency. It is proved that the correlated chaos improve the randomness of the image encryption [39]. A novel image steganography process has been proposed by combining the features of genetic algorithm and cryptography. The process hides confidential data into various digital media formats, such as image, audio and video which is then converted into unreadable format by involving cryptography [30]. It is understood that the output sequence of chaotic encryption system consists of a pair of linear (permutation) and nonlinear (diffusion) conversion processes [8, 11, 14–16, 19, 28, 30, 33–35, 39]. The sequences of linear and non-linear processes may be repeated in order to increase the robustness of cryptography scheme that requires larger computational time and also reduces the performance of the encryption system. The paper presents a secured data embedding process by involving both pixel permutation and chaos encryption techniques. The process starts with transformation of ECG signal and also confidential diagnosis data into QR codes of M × M in size, individually. It is followed by embedding of 2D binary matrix with color image components, R, G and B through pixel permutation process. Eventually, the scrambled color image component is further encrypted through chaotic logistic map in order to ensure better improved security and efficiency. The robustness of the proposed system is validated in terms of number of pixel change rate (NCPR), unified average changing intensity (UACI) and information entropy. The results are found to be better and comparable than other existing cryptography techniques. The paper is organized as: Section 2 presents the details on chaotic map and QR code adapted. The proposed color image encryption algorithm is described in Section 3. The section 4 deals with validation of proposed technique by analysing imperceptibility and security components. The conclusion and future work direction are summarized in Section 5. 2 The preliminary work 2.1 One-dimensional logistic chaotic maps The chaos theory deals with a system with complicated behaviour in which a small change in input results significant impact on output value. The chaotic image encryption process is divided into two major categories namely one dimensional (1D) and multidimensional chaotic systems. The multi dimensional chaotic image encryption process has complex structure as it involves multiple parameters and for the obvious reasons it increases complexity and requires high computational time. The one dimensional (1D) chaotic encryption system are simple in structure, easy to implement and requires minimum computational time and resources. The 1D chaotic process has been improved by various means, such as by modifying 1D chaotic map, by combining two 1D chaotic map sequences and by generating a 2D chaotic map through two 1D chaotic maps [42, 48]. The study employs a 1D chaotic map and it is expressed in the form of order differential equation and it is represented as, X kþ1 ¼ μX k ð1–X k Þ ð1Þ Where, μ and X0 represent system parameter in the ranges from 0 and 4 and initial condition takes value within the range of (0, 1), respectively. The decryption process requires same Author's personal copy Multimedia Tools and Applications system parameters in order to regenerate the same chaotic sequence. Obviously, any change in these parameters results uncorrelated chaotic sequence. The one-dimensional logistic map is generated by using chaotic sequence {Xk; k = 1, 2, 3….N} and is considered to be one of the simple and unique chaotic maps [42, 48]. 2.2 Quick response code (QR code) QR code is a 2D binary matrix or two-dimensional barcode designed and developed by DensoWave in1994 to track vehicles in an automotive industry [13]. In recent years, due to its efficient data handling and error correction capability, it is widely used in several applications, such as medical, advertisements, money transferring, smart phone application programming interface (API) and product labelling etc., The two dimensional barcode represents binary bit 0’s as white dots and 1’s as black dots. The numeric, alphanumeric, binary and kanji are the four standardized encoding modes that determine storage capacity of a QR code. Low ‘L’, Medium ‘M’, Quartile ‘Q’ and High ‘H’ are the four distinct error correction levels which are used to extract information from horizontal and vertical binary image components [13, 22]. The error correction level ‘L’ shows 7% of data retrieval capability with maximum storage capacity, whereas level ‘H’ possesses 30% of data retrieval ability but poor storage capacity [22]. The QR code image contains several functional patterns, such as, finder, separator, timing, alignment, data, format information, error correction and remainder bits which are shown in Fig. 1. The finder patterns are located in three corners of QR code that support the decoder to recognize QR code with correct orientation. The white separator with one pixel width is used to improve recognisability of finder pattern. The black and white modules are known as timing pattern utilized to represent width of single pattern. With increase in embedding capacity the QR code image pattern becomes denser, results image distortion which is governed by alignment pattern. The format information stores the details on type of error correction level and also about masking pattern. The confidential data is converted into equivalent binary stream and it is stored in 8 bit parts of data section. Similarly, the error correction codes are stored as 8 bit long codeword and the remainder bits are represented as empty bits. A QR code of version 40 shows the ability of encoding to the maximum of 4296 alphanumeric data or 7089 numeric data or 2953 binary data or 1817 kanji data with a module size of 177 × 177 [13, 30, 39, 42, 48]. 3 Proposed methodology The proposed work embeds different multimedia information, such as ECG signal and QR code within a cover image and uses 1D logistic map based encryption process as an attempt to enhance the security of conventional steganography processes. The proposed color image encryption process is realized through five major steps: (i) Transforming multimedia information into QR codes or 2D binary matrix (ii) Data embedding by using pixel permutation process (iii) Image encryption through 1D logistic map and (v) Data extraction and signal reconstruction processes. The proposed color image cryptography scheme is shown in Fig. 2. 3.1 Transforming multimedia information into 2D binary matrix ECG signal with 5000 samples at 10 bit resolution obtained from MIT- BIH database is transformed into a 2D binary matrix, i.e., QR code. It is learnt that the quantity of secret data Author's personal copy Multimedia Tools and Applications Fig. 1 Represents functional pattern of QR code that can be encrypted within a QR code is very much limited. Hence, the ECG signal with 5000 samples is segmented into an array of 1000 samples and each segment is embedded into QR code, individually. The proposed ECG signal into 2D binary matrix conversion process is shown in Fig. 3. The direct conversion of ECG signal sample into binary matrix is restricted and not supported by any of the generic available QR code generator/readers. Therefore, the negative samples of an ECG signal are first converted into positive integer values by means of shifting constant and carried out as one of the signal pre-processing steps. It is followed by scaling of floating values through scaling constant which is then normalized by using modulo division process. The preprocessed integer samples are transformed into an array of m bit binary sequence. Further, concatenation of each M bit binary sequence into equivalent n pairs Fig. 2 Shows QR code steganography along with image encryption Author's personal copy Multimedia Tools and Applications Fig. 3 Shows ECG signal to QR code transformation process (N = M/2) is carried out. Each pair of binary sequence is encrypted into alphanumeric cipher text. Eventually, the cipher text data is encoded within a binary matrix by using a generic QR code generator. The flow chart of the ECG encryption is shown in the Fig. 4. At the end of this step, ECG signal with 5000 samples are concatenated and it is encrypted into 5 QR code version of 40 which is M × M in size, i.e., IE = {QR1, QR2, QR3, QR4, QR5} where, IE represents ECG signal with 5000 samples, QR1, QR2, QR3, QR4, QR5 are the individual QR code with 1000 ECG samples. Similarly, the patient confidential information, including name, age, sex and diagnose report are transformed into equivalent 2D binary matrix by using a QR code generator. The patient information, IC in the form of alphanumeric data is one of the common standardised QR encoding modes that determines capacity of data container, i.e., I C = QR 6 Where,ICrepresents patient information and QR6 specifies the QR code with patient information. Eventually, the generated QR codes of M × M array are transformed into 1 × 2 M in size. Fig. 4 Shows flow graph to represent steps involving ECG signal to QR code conversion process Author's personal copy Multimedia Tools and Applications 3.2 Data embedding using pixel permutation The proposed process embeds binary information into major color image components, such as red (R), green (G) and blue (B) of N × N in size, separately. The pixel scrambling is considered as first process of permutation which is shown in Fig. 5. The red color image component, CR (i, j) at N × N pixel array is shuffled into CR (k-i, j) where, the value of k is N + 1. Simultaneously, the blue and green components, CB (i, j) and CG (i,j) with N × N size are scrambled into CB (k-j, i) and CG (k-i, k-j), respectively. Further, the encrypted QR codes, one with confidential data, QR6 and five QR codes with ECG signal, QR1, QR2, QR3, QR4, QR5 of M × M in size are transformed into an array of 1 × 2 M. Each individual color image component i.e., R, G and B is used to embed two different binary arrays of 1 × 2 M and the overall data embedding process is illustrated in Fig. 6. As mentioned, the 2D binary matrices, (QR1, QR6)integrated with confidential data and ECG signal are embedded into red color image component. Similarly, the blue and green color image components are successfully used to embed remaining QR codes,(QR2, QR3) and(QR4, QR5). The binary data embedding process by involving pixel permutation in the respective color image component is shown in the Fig. 7. The red color image component is chosen to illustrate the functional aspect of binary bit embedding process through pixel permutation. As mentioned, the red image component of N × N array is first segmented into equivalent 2 × 2 arrays and the QR code with confidential Fig. 5 (a) shows color image component of pixel, (b) shows red image component after CR(k-i,j) pixel transformation, (c) shows blue image component after CB(k - j, i) pixel transformation, (d) shows green image component after CG (k-i, k - j) pixel transformation Author's personal copy Multimedia Tools and Applications Fig. 6 Shows data embedding process using pixel permutation data of M × M in size is transformed into binary array of 1 × 2 M in size and it is explained in the Eq. (2)
 ð2Þ I c ¼ Bcn ; Bcn ¼ Bc1 ; Bc2 ; Bc3 ::…; Bcn : Where, Icrepresent QR code with confidential data and Bcn is binary array of 1 × 2 M in size. Similarly, the QR code with ECG signal of M × M in size is transformed into binary array of 1 × 2 N in size and it is explained in the Eq. (3)
 ð3Þ I E ¼ Ben ; Ben ¼ Be1 ; Be2 ; Be3 ::…; Ben : Where, IErepresent QR code with ECG signal and Ben is binary array of 1 × 2 M in size. Each bit of Bcn binary array is paired with binary bit ofBen array and generates a two bit binary pair array of 1 × 2 N in size. Further, each binary pair is embedded within 2 × 2 pixel array of color image component by considering the scenario and it is shown in Table 1. The binary data embedding process by using pixel permutation is illustrated here. With respect to the binary pair obtained from Bcn andBen , the proposed data embedding process swaps its pixel position in the 2 × 2 array. For example, if the binary bit from Bcn and Ben are 0 then the respective numeric key 1 is generated that results no change in pixel position of 2 × 2 array. If the binary bits of Bcn is 0 and Ben is 1 then the corresponding key 2 is generated that results swapping of pixel position Ci, j, Ci, j + 1and Ci + 1, j, Ci + 1, j + 1 . Similarly, if the binary bits of Bcn is 1 and Ben is 0 then the corresponding key 3 is generated that initiates swapping of pixel Ci, j, Ci + 1, jandCi, j + 1, Ci + 1, j + 1. Eventually, if the binary bit from Bcn and Ben are 1 then the respective numeric key 4 is generated that results swapping of pixel position Ci, j, Ci + 1, j + 1and Ci, j + 1, Ci + 1, j in 2 × 2 array of plain image component. The generated key can be alphabets, numbers and Author's personal copy Multimedia Tools and Applications Fig. 7 Shows steganography using pixel permutation process special characters which is used to find the nature of pixel modification and the embedded binary pair. 3.3 Image encryption The image encryption process considers the following iterative steps. Step 1: The color image of N × N in size is separated into R, G and B image components. As stated, the individual color image components are embedded with binary confidential data by using pixel permutation process. Step 2: In this step, each color image components is individually encrypted by using 1D logistic chaotic encryption process. The stego image component with N × N array is transformed into 1 × 2 N array which is followed by computation of logistic map process through initial and controlling parameters. For example, R image component of size N × N is transformed into an array of 1 × 2 N in size. Table 1 illustrates the embedding condition in 2 × 2 array Bcn Ben Key Action to be taken 0 0 1 1 0 1 0 1 1 2 3 4 no change in pixel position Swap Ci, j, Ci, j + 1and Ci + 1, j, Ci + 1, j + 1 Swap Ci, j, Ci + 1, jand Ci, j + 1, Ci + 1, j + 1 Swap Ci, j, Ci + 1, j + 1and Ci, j + 1, Ci + 1, j Author's personal copy Multimedia Tools and Applications Step 3: The initial parameter value, X(0) is used to generate chaotic sequence whereas controlling parameter μ determines chaos sequence,X = {X1, X2, X3. . …X1 × 2N} between 0 and 1 which is then normalized within the range between 0 and 255. Step 4: Calculate the diffusion matrix D = {D1, D2, D3, .. …D1 × 2N}by using the Eq. (4),     DðiÞ ¼ mod floor X ðiÞ  1014 ; 256 ð4Þ Step 5: The encrypted image pixel array, E = {E1, E2, E3, .…E1 × 2N} is calculated by considering the Eq. (5),   0 EðiÞ ¼ mod DðiÞ⊕C ðiÞ ð5Þ Where, C′(i) is the image after pixel permutation, D(i) is the diffusion matrix and E(i) is the encrypted image Step 6: Convert array, E of 1 × 2 N in size into R color image of N × N in size. Step 7: Similarly repeat the steps from 2 to 6 for the remaining G and B stego image components. Step 8: Eventually, each encrypted image components are convolved into R, G and B of original color image. 3.4 Image decryption The similar but reversible sequences have been carried out during the decryption process. It is started from encrypted cipher image which is once again separated into R, G and B image components and each image component is individually decrypted with diffusion matrix generated by using initial value X (0) and controlling parameter μ. The process involved in the decryption is expressed in Eq. (6). 0 C ðiÞ ¼ modðDðiÞ⊕E ðiÞÞ ð6Þ The generated key and the individual pixel position of decrypted image components are c equally used for the extraction of binary arrays (1 × 2 M) with confidential data, Bn and also e ECG signal,Bn . The decrypted binary arrays are then converted into QR codes of M × M in size which is given to generic QR code reader to extract the original confidential data and as well as ECG signal. 4 Simulation results The experimental results and also different validation strategies are presented to demonstrate the performance of the proposed color image cryptography scheme. The security feature of the proposed process is evaluated in terms of statistical attack, differential attack and also bruteforce attacks. The simulation result of the proposed encryption process is shown in the Figs. 8 (a) and (b). Author's personal copy Multimedia Tools and Applications 4.1 Statistical attack 4.1.1 Histogram analysis The distribution of the pixel intensity values within an image is represented in terms of histogram. Figures 9 (a) from (f) show the histogram of R, G and B color image component for the cipher image, pepper. In general, the color image pixel values of R, G and B components are determined by its intensity values. The results observed that histograms of cipher image components are uniformly distributed that shows its ability to resist statistical attack. The histogram analysis of R, G, and B color image components of both plain and encrypted images are shown in Figures from 9 (a) to (f). The random and uniform distributions of histogram values of plain and encrypted images have showed significant dissimilarity from each other in the interval between 0 and 255. It could be considered as an evident that no useful information can be extracted from the encrypted image. 4.1.2 Correlation analysis The horizontal, vertical and also diagonal directions of plain and cipher images are analysed through its correlation coefficients in order to evaluate the image randomness between two adjacent pixels.. In this study, the correlation analysis is considered 1000 pairs of adjacent pixels in each direction are calculated using the Eq. (7, 8, 9 and 10). Figs. 10 (a), (c) and (e) shows the correlation distributions in three different directions for the plain image and are found to be highly concentrated, i.e., the plain image pixels are strongly correlated. Figs. 10 (b), (d) and (f) illustrates the correlation distribution of cipher image which are observed to be random i.e. the cipher image contains low pixel correlation in all three directions. covðx; yÞ rxy ¼ pffiffiffiffiffiffiffiffiffiffipffiffiffiffiffiffiffiffiffiffi ; DðxÞ DðyÞ covðx; yÞ ¼ 1 N ∑ ðxi −E ðxÞÞðyi −EðyÞÞ N i¼1 ð8Þ 1 N ∑ xi N i¼1 ð9Þ 1 N ∑ ðxi −E ðxÞÞ2 N i¼1 ð10Þ E ð xÞ ¼ DðxÞ ¼ ð7Þ Where, N is set to 1000 as mentioned above, x and y is the two adjacent pixels in three different directions. The resultant correlation coefficient values of plain and cipher images in all direction are shown in Table 2. The coefficient values of plain image are found to be closure to 1 in all the directions, namely horizontal, vertical and diagonal whereas cipher image shows the correlation coefficient values at an average of 0. It means encrypted cipher image has very negligible correlation values. It is observed that correlations between adjacent pixels have been significantly reduced. Author's personal copy Multimedia Tools and Applications Fig. 8 (a) The color plain image ‘pepper’ (b) color cipher image ‘pepper’ Fig. 9 Histogram analysis of R, G, B image component for plain and cipher image of pepper. (a) Histogram of R image component of plain image. (b) Histogram of R image component of cipher image. (c) Histogram of G image component of plain image. (d) Histogram of G image component of cipher image. (e) Histogram of B image component of plain image. (f) Histogram of B image component of cipher image Author's personal copy Multimedia Tools and Applications Fig. 10 shows correlation of two adjacent pixel in (a) horizontal direction of plain image (b) horizontal direction of cipher image (c) vertical direction of plain image (d) vertical direction of cipher image (e) diagonal direction of plain image (f) diagonal direction of plain image The performance of proposed encryption technique is compared with other existing cryptography processes and the results of correlation analysis in horizontal, vertical and diagonal directions are shown in Table 3. Table 3 illustrates the comparison of correlation coefficient analysis between proposed and existing encryption algorithms for the cover image Lena with 512 × 512 × 3 in size by considering horizontal, vertical and also diagonal directions [5, 6, 10, 18, 22, 25, 26, 39, 41, 43, 44, 47]. From the experimental results, it is observed that the difference in correlation coefficient values between the proposed scheme and the ideal values of horizontal, vertical and diagonal directions are very much nearer to zero. Further, the correlation coefficient of cipher image obtained through the proposed algorithm is observed to be very minimum than all other known algorithms. The results have proved that the proposed algorithm showed better resistance against statistical attack. 4.1.3 Information entropy analysis The information entropy analysis is performed in order to evaluate the uncertainty in image and also to measure randomness in encrypted image. The entropy value nearer to eight is considered as effective encryption scheme and the value closure to zero shows poor encryption Author's personal copy Multimedia Tools and Applications Table 2 Shows correlation coefficient of plain and cipher images Image Pepper Lena Baboon Plain Cipher Plain Cipher Plain Cipher Horizontal Vertical Diagonal 0.9814 −3.8965e-04 0.9798 6.1960e-04 0.9178 7.3419e-04 0.9852 8.4054e-04 0.9902 2.6204e-04 0.8581 0.0012 0.9734 −6.7966e-04 0.9718 −0.0014 0.8369 −4.3894e-04 process. The information entropy evaluated for the proposed encryption algorithm and it is found to be very much closure to 8. The expression for estimating the entropy is given in Eq. (11). M −1 H ðmÞ ¼ ∑ pðmi Þlog i¼0 1 pðmi Þ ð11Þ Where, M is the total number of symbol in information source, mi is the information source and p(mi) is the probability of symbol mi.. From the literature, it is understood that an information source with 256 symbols and also entropy close to 8 that signifies less possibility for the attacker to decrypt the encrypted image. Table 4 shows the obtained information entropy values for Pepper, Lena and Baboon images which are found to be very nearer to the ideal value of 8. It is further estimated that the difference in entropy value between the proposed scheme and the ideal value is approximately 0.00875%. 4.2 Differential attack The sensitivity of a secured cryptosystem is measured from its responsiveness even for one pixel change to be occurred either in secret key or plain image. Similarly, the characteristics of a cipher image completely can be modified by making a small variation in plain image or secret key during data encryption process. Hence, it is considered very important to validate Table 3 Comparison of correlation coefficient analysis between proposed and other known cryptography techniques Algorithm Lena (512 × 512) Plain image Proposed Safwan & Mousa [22] Yang et al. [25] Wong et al. [5] Wang et al. [44] Mazloom et al. [18] Akhshani et al. [39] S. M. Seyedzadeh et al. [41] S. M. Seyedzadeh et al. [26] Cipher image Horizontal Vertical Diagonal Horizontal Vertical Diagonal 0.9798 0.9923 0.9802 0.9751 0.9759 0.9759 0.9759 0.9759 0.9759 0.9902 0.9965 0.9866 0.9889 0.9848 0.9848 0.9848 0.9848 0.9848 0.9718 0.9871 0.9646 0.9670 0.9624 0.9624 0.9624 0.9624 0.9624 0.0006 −0.0015 −0.0020 0.0068 0.010889 0.007539 0.004312 0.000550 0.001109 0.0002 0.0015 0.0161 0.0078 0.0181 0.0128 0.0054 0.0008 0.0007 −0.0014 −0.0014 0.0178 0.0032 0.0061 0.0049 0.0072 0.0011 0.0008 Author's personal copy Multimedia Tools and Applications Table 4 Shows information entropy of encrypted image Image Red Green Blue Pepper Lena Baboon 7.9994 7.9993 7.9994 7.9993 7.9994 7.9993 7.9993 7.9994 7.9993 the resistance of the proposed algorithm against differential attack and also to estimate the changes occurred in both cipher as well as plain images. The number of pixel change rate (NCPR) represents total number of pixels changes occurred in cipher and plain images. In general, the cryptosystem is considered as more sensitive towards one pixel change in cover image found with an optimum NCPR value as much as closure to 100%. The estimation of unified average changing intensity (UACI) is used to understand the average intensity differences between plain and cipher images. It is understood that the cryptosystem with more effective resistance to differential attack reflects an optimum UACI value to the maximum of 33%. The following Eqs. (12) and (13) are used to measure NCPR and UACI, respectively. H W 1 ∑ ∑ Dði; jÞ W  H i¼0 j¼0 0 if C 1 ði; jÞ ¼ C 2 ði; jÞ Dði; jÞ ¼ 1 f C 1 ði; jÞ≠C 2 ði; jÞ NPCR ¼ UACI ¼ ð12Þ W H jC ði; jÞ−C ði; jÞj 1 1 2 ∑ ∑ W  H i¼0 j¼0 255 ð13Þ Where, W and H represent width and height of the cipher image before and after changes occurred in one pixel, C1 and C2 denote cipher image before and after one pixel change, respectively. D(i,) = 1 defines unequal pixel position of C1(i, j) and C2(i, j) else D(i, j) is considered as 0. Table 5 shows the obtained values of NPCR and UACI for three different images at round 1 and 3. It is found that merely one round is more than sufficient to achieve optimum value of NPCR and UACI that proves the efficiency and also robustness of proposed cryptography technique. The performance of proposed technique is compared with other existing chaos based crypto schemes by considering different standard test images. Table 6 shows the obtained NCPR values for Lena and baboon images are compared with existing algorithms described in [37– 39]. It should be noted that the proposed process modifies pixel position during the permutation Table 5 Shows obtained results of NCPR and UACI for the standard test images Round 1 Image Red Green Blue Red Green Blue 0.9959 0.9963 0.9961 0.3351 0.3339 0.3343 0.9961 0.9960 0.9962 0.3339 0.3348 0.3347 0.9981 0.9969 0.9950 0.3901 0.3775 0.3378 0.9963 0.9960 0.9960 0.3345 0.3346 0.3352 0.9963 0.9960 0.9961 0.3346 0.3346 0.3351 0.9981 0.9969 0.9950 0.3897 0.3769 0.3384 NCPR UACI Pepper Lena Baboon Pepper Lena Baboon 3 Author's personal copy Multimedia Tools and Applications process to ensure high randomness in an encrypted image and the test is considered the values of NCPR and UACI at first round. The proposed technique shows relatively minimum impact when changes in pixel occurred in the plain image than the methods described in [18, 39]. However, the process has been presented by Wang et al., [39] showed relatively better results than the technique proposed in this study. The reasons, possibly due to higher round number (R), 30 and also the plain image pixel is modified in the lowest bit in Wang et al., [39]. However, the obtained information entropy for the test images through proposed technique relatively shows the better results than all other existing techniques, including [39]. In addition, the traditional encryption schemes [18, 39, 43] with R, G and B image components of Lena and Baboon are compared with the proposed scheme, where the mean entropy, NPCR and UACI values of the R, G and B image components (Lena and Baboon) are found to be in an average of 0.996, 0.334 and 8, respectively. From the experiment results shown in Table 6, it is found that a small change to be occurred in the plain image can result the considerable changes in cipher image. For example, the NPCR value, 0.996 of plain image shows that one pixel change in cover image can alter 99.6% pixels in the resultant cipher image. The proposed encryption scheme employs pixel scrambling and pixel permutation to embed 2D binary matrix in R, G and B image components, where entire plain image pixels are relocated but their values remain unchanged. Further, it is diffused by involving 1D chaotic map in order to ensure poor relationship between plain image and the cipher image. Hence, the proposed scheme requires only one round to achieve optimum values of NPCR and UACI. The percentage difference in metric values between the proposed scheme and the ideal values are found to be 0.030% (NPCR) and 5.05% (UACI). Table 6 Shows of NCPR, UACI and Entropy values of existing and proposed method NCPR UACI Entropy Algorithm Plain image Red Green Blue Proposed Kalpana & Murali [18] Xiaopeng et al. [43] Xingyuan Wang & Hui-li Zhang [39] Proposed Kalpana & Murali [18] Xiaopeng et al. [43] Xingyuan Wang & Hui-li Zhang [39] Proposed Kalpana & Murali [18] Xiaopeng et al. [43] Xingyuan Wang & Hui-li Zhang [39] Proposed Kalpana & Murali [18] Xiaopeng et al. [43] Xingyuan Wang & Hui-li Zhang [39] Proposed Kalpana & Murali [18] Xiaopeng et al. [43] Xingyuan Wang & Hui-li Zhang [39] Proposed Kalpana & Murali [18] Xiaopeng et al. [43] Xingyuan Wang & Hui-li Zhang [39] Lena Lena Lena Lena Baboon Baboon Baboon Baboon Lena Lena Lena Lena Baboon Baboon Baboon Baboon Lena Lena Lena Lena Baboon Baboon Baboon Baboon 0.9960 0.9965 0.9932 0.9961 0.9960 0.9974 0.9917 0.9960 0.3345 0.3311 0.3122 0.3344 0.3352 0.3335 0.3135 0.3343 7.9993 7.9962 7.9928 7.9974 7.9994 7.9968 7.9945 7.9972 0.9960 0.9954 0.9929 0.9961 0.9961 0.9959 0.9927 0.9960 0.3346 0.3399 0.3142 0.3352 0.3351 0.3362 0.3125 0.3344 7.9994 7.9993 7.9912 7.9970 7.9993 7.9964 7.9920 7.9969 0.9969 0.9966 0.8930 0.9961 0.9950 0.9965 0.9923 0.9961 0.3897 0.3389 0.3136 0.3350 0.3384 0.3390 0.3130 0.3344 7.9994 7.9995 7.9932 7.9971 7.9993 7.9960 7.9932 7.9969 Author's personal copy Multimedia Tools and Applications 4.3 Key space analysis It is learnt that any good encryption algorithm should have large key space in order to resist brute-force attack. The proposed cryptography system with initial condition,(μR, μG, μB)and system parameters(X0R, X0G, X0B) has computer precision of10−14. The total key space has been calculated as(1014 × 1014 × 1014 × 1014 × 1014 × 1014)R ≈ 1084 × R ≈ 2280 × R which is larger in size that ensured the high resistance against brute-force attack, where R represent round number. The round number of the cryptosystem is also an important factor that determines system security and also its performance. The increase in round number will increase the size of key space and also computational load. The proposed algorithm is found to be showing better resistance against brute-force attack even within a single round of analysis. Hence, the computational load is observed to be small. It is understood that key space should be higher than 2100 to show the better efficiency and effectiveness of the cryptography techniques. Table 7 shows the evaluated key space and also corresponding level of round number for different encryption algorithms. The proposed algorithm shows the better key space of 2280 and also it has produced the optimum values of both NPCR and UACI within first round analysis. Though all other known algorithms are found with better key space and also NPCR and UACI; have required minimum of 2 round number analyses [10, 37, 38, and]. The metrics, such as NPCR and UACI are found to be very minimum than required UACI>0.333 and NCPR >0.996, invariably in all other works at round number (R); hence, round number have been iterated still achieving better metrics. Obviously, the iteration sequences require more computational resources which increase cost as well as complexity. 4.3.1 Key sensitivity analysis In general, the efficiency of a good encryption algorithm is measured by its sensitiveness towards tracking of changes to be occurred with encryption keys. The sensitivity has been evaluated by making a small change in any one of the keys(μR, μG, μB, X0R, X0G, X0B), to be chosen, very randomly. The decrypted plain image by using the exact decryption keys is shown in Fig. 11 (a). As illustrated, it is presumed that making even a small change in any one of the keys should alter the characteristics of the plain image, significantly. The presumption is proved by considering the changes in different key values. For example, the changes have been made in secret keys (μR, μG, X0B)whereas other keys (X0R, X0G, μB) unaltered. As presumed, the Table 7 Shows comparative analysis of key space and round number in different algorithm Algorithm Key space Round number (R) NPCR UACI Proposed Yavuz, Erdem, et al. [48] Leynan wang [42] Tang’s et al. [50] Xiaopeng et al. [43] Wang, Xingyuan et al. [39] Wang, Yong, et al. [40] Li, Yueping [23] 2280 2194 × R 2640 2451 2233 2183 2128 2273 × R 1 3 10 60 2 4 2 2 >0.996 >0.996 >0.996 >0.996 >0.996 >0.996 >0.996 >0.996 >0.333 >0.333 >0.333 >0.333 >0.333 >0.333 >0.333 >0.333 Author's personal copy Multimedia Tools and Applications changes result completely disturbed plain image and it is shown in Fig. 11(b). The decryption process is tried with incorrect keys(μR, X0G, X0B)whereas other keys (X0R, μG, μB)have been unchanged and the resultant impact is shown in Fig. 11(c). Similarly, Fig. 11(d) shows the resultant decrypted image with incorrect decryption keys (X0R, μG, X0B)and other keys (μR, X0G, μB) remain unchanged. It is evident that the proposed technique has showed maximum sensitivity towards original key information. 4.4 Computational speed analysis The functionality of an algorithm in real-time and practical scenarios can be estimated by considering the requirements of computational resources as well as throughput. The proposed process is implemented in Matlab R2012a by using a computer with Intel core i3 2.40 GHz processor, 4 GB RAM and 500 GB hard disk running on windows 7 operating system and benchmark color images size of 512 × 512. Table 8 summarizes the observed average execution time periods for different algorithms including the proposed technique. It is observed that the proposed technique relatively runs very much faster than [4, 37, 38, 45] but slower than [2, 26, 41, 50]. The present study considers multi-stage secured transmission of highly sensitive bio-signal along with Fig. 11 (a) decrypted image with correct secret key, (b) decrypted image with slight change in secret keys (μR, μG, X0B), (c) decrypted image with slight change in secret keys (μR, X0G, X0B) (d) decrypted image with altered secret keys (X0R, μG, X0B) Author's personal copy Multimedia Tools and Applications Table 8 Shows average execution time Algorithm Execution Time (s) Proposed algorithm Wang et al. [41] Mazloom, et al. [26] Tang’s et al. [50] Akhshani et al. [2] S. M. Seyedzadeh et al. [37] S. M. Seyedzadeh et al. [38] Arnold transform algorithm [4, 45] 24 8.6573 9.2115 9.656 15.3475 41.1227 44.9389 955.5075 diagnosis data across the communication networks. The average execution time for encryption and data hiding process is calculated in an average of 24 s for a color image with 512 × 512 in size. From literature, it is found that some chaos based encryption scheme with high security shows very low execution time [40–47]. However, in this proposed encryption scheme both data hiding and encryption are realised, where six different binary matrixes are embedded within a color image components. Further, it is individually encrypted by using chaotic map in order to ensure better security. Hence, the algorithm is found to be with at par execution time when compared to other experiments. The image encryption methods have been described in [&, 10, 44, 47] involved with a fast, simple and robust 2D chaotic system for the diffusion of encrypted image. In [37 & 38], the resultant of a multiple hyper- chaotic map is used for the encryption of color image components by considering DNA encoding rules. A novel color image encryption scheme with heterogeneous bit-permutation and correlated chaos by using 1D chaotic map is introduced in [39]. The chaos color image encryption has effectively reduced the correlation between R, G and B image components by means of combined permutation and also diffusion stages that are presented in [41]. A novel 1D nonlinear chaotic map with power and tangent functions to promote nonlinearity property to a new Coupled Nonlinear Chaotic Map (CNCM) is suggested in [26]. Multiple gray scale images have been decomposed into bit planes and are randomly swapped and encrypted by using chaotic map is presented in [50]. In [44 & 45], the author have proposed a Coupled Two-dimensional Piecewise Nonlinear Chaotic Map (CTPNCM) and quantum logistic map to diffuse the relationship of pixels are in color components in order to ensure better security, sensitivity and throughput of image encryption scheme. The study involves a performance comparison of all these mentioned algorithms involve different chaos encryption schemes with the proposed technique are illustrated in Tables 3, 6, 7 and 8. 4.5 Data as well as signal reconstruction process In any cryptography technique, the complete reconstruction of original image and secret information is considered very much important. The proposed technique uses QR code as its data container which is known for its strong fault tolerance and also error correction ability. Hence, it is presumed that the QR code helps to retrieve the original ECG signal along with diagnose data, completely. The presumption is proved with the results obtained. The impeccability between original and reconstructed ECG Author's personal copy Multimedia Tools and Applications Fig. 12 Shows ECG signal obtained from MIT BIH data base and reconstructed ECG signal signal is observed to be very perfect and the over imposing of these two signals is shown in Fig. 12. In addition, the amount of deterioration in reconstructed signal is evaluated in terms of percent root mean square difference (PRD). From the literature, it is learnt that PRD value of the reconstructed ECG signal within the range between 0 and 9% are suitable for the diagnosis purposes. The PRD value estimated by using the proposed technique for the different reconstructed ECG signals is found to be at an average of 3.43%, which is shown in Table 9. However, the proposed scheme is found to be with increased computational cost due to pixel permutation process and also chaotic map analysis. The laboratory considers the possible steps to minimize the computational time in the near future. Table 9 Shows PRD values obtained for different ECG signal ECG Signal PRD (%) 16,265 16,272 16,273 16,420 16,539 17,052 18,184 19,088 19,090 Average 2.61 4.84 1.69 3.95 4.73 5.09 3.24 2.91 2.85 3.43 Author's personal copy Multimedia Tools and Applications Table 10 Comparison of the proposed image encryption method with other recent encryption algorithms Algorithm Key space Entropy Correlation (CC) NPCR UACI Ideal value Proposed Kalpana & Murali [18] Xiaopeng et al. [43] Xingyuan Wang & Hui-li Zhang [39] >2100 2280 >2300 2233 2183 ≈8 7.9993 7.9998 7.9924 7.9972 ≈0 0.0007 0.0221 0.0010 0.0051 ≈99.6 99.63 99.61 95.97 99.61 ≈33.4 35.29 33.66 31.25 33.49 5 Discussion It is understood that an ideal encryption scheme should have a total key space value greater than 2100 in order to resist brute-force attack. The key space value of the proposed cryptosystem is found to be 2280, that is large enough to resist the attacks. For example, the fastest computer till date performs 280 computations per second [27, 31], the computational load can be calculated as 2280 ¼ 5:09  1052 years 280  365  24  60  60 ð14Þ In addition, the proposed scheme has obtained its optimum ideal key space value with single round of encryption which is presented in Tables 7 and 10. The statistical attacks, such as histogram and correlation analysis are used to remove intrinsic features of plain images. The histogram of cipher image components are uniformity distributed that is totally different from plain image components and the correlation between adjacent pixels in cipher images are also close to ideal value 0. Hence, the proposed scheme is found to be strong enough to resist similar statistical attacks. The NPCI and UACI values of proposed algorithm are very close to the ideal values, therefore the proposed scheme has adequate security against differential attack. Eventually, the PRD value of reconstructed signal is also within the ideal value of 9%. Hence, the reconstructed ECG signal can be used for diagnose purpose. 6 Conclusions The paper describes a novel digital data embedding process by combining the features of pixel permutation and chaos encryption techniques. The binary information is embedded inside the red, green and blue components of cover image by using pixel permutation process that results higher randomness. Also, 1D chaotic sequence process is found to be further strengthening the cryptography technique. It is observed that the proposed algorithm requires merely one round iteration to exhibit extreme sensitivity to one bit change occurred in plain image. The reliability and robustness of the proposed technique are validated through various analyses, such as histogram, key space, correlation, differential attack and computational speed. 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Yang H, Wong K-W, Liao X, Zhang W, Wei P (2010) A fast image encryption and authentication scheme based on chaotic maps. Commun Non linear Sci Numer Simul 15(11):3507–3517 48. Yavuz E et al (2016) A chaos-based image encryption algorithm with simple logical functions. Comput Electr Eng 54:471–483 49. Zhai X et al. (2016) ECG encryption and identification based security solution on the Zynq SoC for connected health systems. J Paral Distrib Comput 50. Zhenjun T, Juan S, Xianquan Z, Ronghai S (2016) Multiple-image encryption with bit-plane decomposition and chaotic maps. Opt Lasers Eng 80(5):1–11 Author's personal copy Multimedia Tools and Applications P. Mathivanan - received his B.E degree in Electronics and Communication Engineering from St. Peter’s Engineering College, Affiliated to Anna University, Chennai, India in 2009. He received his M.E degree in Applied Electronics from Sri Ram Engineering College, Affiliated to Anna University, Chennai, India in 2012. Currently he is working as Assistant Professor in the Department of Electronics and Communication Engineering at Velammal Engineering College, Chennai, India and pursuing his PhD in the field of Bio-Signal Processing from Anna University, Chennai, India. His research interests are Bio-Signal Processing, and Image Processing. A. Balaji Ganesh has completed his Doctoral study in the area of sensors and instrumentation from National Institute of Technology in the year 2007. He was as TEQIP fellow during the period from 2008 to 2011. He was an awardee of Career award for young teachers (CAYT) from All India Council for Technical Education (AICTE), New Delhi. He has actively involved in various funded projects received from Department of Science and Technology (DST), New Delhi. His research interests include, optical sensors and instrumentation, wireless embedded systems and cognitive sensor fusion. Now he is with TIFAC-CORE as Co-coordinator at Velammal Engineering College, Chennai-66, INDIA.