Electrical & Electronics Engineering, SASTRA University, Thanjavur, Tamil Nadu, India.

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1 Volume 119 No , ISSN: (on-line version) url: Fuzzy Inference System for adaptive data embedding in compressive sense C. Lakshmi 1, N.Nivetha 2, S.Nithya 2, K. Thenmozhi 1, and Rengarajan Amirtharajan 1 1 Faculty & 2 Students,Department of Electronics and Communication Engineering, School of Electrical & Electronics Engineering, SASTRA University, Thanjavur, Tamil Nadu, India. Abstract: ' lakshmi_c@ece.sastra.edu@ ece.sastra.edu *Corresponding author: C. Lakshmi Recent days most of the information are transmitted over internet. Data security plays important role in information sharing over internet. There was a problem with this data transmission over open channel such as copy right, hackers and so on. Communication might be effective if it secured. Information hiding is a way to achieve data security by hiding the secret data into cover medium. In recent time artificial intelligence and fuzzy logic have created the impact on entire science area. In this paper important characteristics of image was taken into account that changes in high frequency components was less sensitive than changes made in low frequency component. This scheme utilizes the fuzzy logic for adaptive edge detection and Arithmetic coding for secret data compression which enhances the payload. This scheme has been verified with various images and different size of payload. Results have been proved the effectiveness of the proposed method. Keywords: Fuzzy inference system (FIS), Arithmetic coding, soft and hard edge detection 1. Introduction: LSB insertion techniques modify the portion of LSBs of the image pixel with the secret bits of same length [1]. The entire pixel in the image will not have equal tolerance to accept the modification.hsv aided LSB method to overcome this problem [2]. Similar way embedding might be done in transform domain, frequency of the pixel has grouped as high and low frequency group and anything might be a elected group for embedding as concern of perceptual transparency of watermarked image or robustness of secret data respectively [3,4]. Edge detection methods assist to choose appropriate pixel for higher payload. Because pixel belonging to edge area has less sensitive to the human eye than pixel belonging to the smooth area [3, 4]. Pixel Variable Differencing (PVD) technique is another way to notify the pixel difference between the neighbors to decide the number bits to be embedded [6, 7]. Optical Pixel Adjustment Process (OPAP) reduces the MSE with higher pay load [8]. Rubik s cube enhances the complexity of the hiding way in terms of randomizing the road path for pixel modification [9]. 447

2 Adaptive way of embedding is required to achieve higher payload without affecting the PSNR. This can be attained by the method which identifies the appropriate pixel withstand against higher modification. Fuzzy [5] logic is the best way to achieve the above. Lossless compression will be an additional support to reach the goal. Hence this paper focuses the followings Fuzzy rule based image edge detector Adaptive embedding technique Higher embedding capacity through arithmetic coding 2. Proposed Method: Figure.1: Intelligent embedding scheme Figure.1 enlightens the significance of FIS and Arithmetic coding in variable secret data embedding. Proposed Algorithm: Phase.1: Edge detection using FIS Step 1: Convert the given color image into gray image I[x, y] to obtain the proper luminance value Step 2: Obtain the image gradient along with both x and y axis Step 2.1: Choose the scaling factor with respect to the gray image. Step 2.2: Choose the kernel for convolution such as 448

3 Gx 1 1 (1) Gy Gx (2) Step 2.3: Find the convolution between the I[x, y] and G X & I and G Y I[ m. n] GX [ m, n] I[ x, y] G[ m x, n y] (3) x y Step 3: Create a fuzzy inference system (FIS) for edge detection Step 3.1: Convert input variables into fuzzy variable using Gaussian membership function k x G( x : k, ) e (4) Where k parameter that determines the rate at which for each x the function increases or decreases with increasing difference ( x) and is the value for which membership grade is 1. Step 3.2: Choose the fuzzy output variable Iy Step 4: Specify FIS Rules r1 soft edge if I X & I y 0 (5) r2 hard edge if I & I 0 (6) X y Step 5: Evaluate the output of the edge detector for each row of pixels in I using corresponding rows of Ix and Iy as inputs. Fuzzy edge detector classifies the pixels into three clusters such as non edge pixels, soft edge pixels, hard edge pixels. Phase.2: Lossless compression on secret text Step 6: Get the first character and find the probability for the character between 0 to 1. Step 7: Find the next upcoming character and sub range related to previous character. Step 8: Find the encoded tag for the word. Step 9: Repeat the step 6 to step 8 for the remaining words till the last word of the sentence which to be transmitted. Phase.3: Embedding the compressed text Step 10: Chop the Compressed text into number of pieces, each piece may have 2, 3 or 4 bits. 449

4 Step 11: Embedding the secret data of 2 bits, 3 bits and 4 bits into non edge, soft edge, and hard edge respectively. 3. Results and Discussion: The proposed method analyzed with five number of images such as Krishna, Penguins, Bear, Superman, City of size of 256 X 256 and different size of secret text have been taken to verify the embedding capacity. (a) (b) (c) (d) (e) Figure.2: Sample input image: (a) Krishna (b) Penguins (c) Bear (d) Superman (e) City (a) (b) (c) (e) (f) (g) Figure.3: (a,e) Input image (b,f) Edge highlighted image (c,g) Watermarked image 450

5 Figure.3 conveys that watermarked image is much similar to the original image, this proves that hard edges withstand the higher payload than soft edges. Figure.4: Fuzzy output rules Figure.4 expresses that comparing the gradient of every pixel in the x and y directions. If the gradient for a pixel is not zero, then the pixel belongs to an edge (black). Table.1: Perceptual transparency between original and watermarked images 1.1 Using Arithmetic code: Image Text Size= 100 Text Size=75 Text size=50 Text size=25 Text size=20 MSE PSNR MSE PSNR MSE PSNR MSE PSNR MSE PSNR Krishna Penguins Koala Vivekanada Lighthouse Without Arithmetic code Image Text Size= 100 Text Size=75 Text size=50 Text size=25 Text size=20 MSE PSNR MSE PSNR MSE PSNR MSE PSNR MSE PSNR 451

6 Krishna Penguins Koala Vivekanada Lighthouse FIS edge detection technique for data embedding is verified with and without lossless compression and the results are tabulated in Table 1.1 and Table 1.2.These tabulated values are proved that FIS edge detection technique supports higher embedding capacity and additionally arithmetic lossless compression enhances the payload. 4. Conclusion: References: Conventional method such as hard thersholding is not right way to identify the image edges, because images should not have sharp boundary for edge detection. This proposed method have grouped the pixels into three clusters such as smooth pixels, soft edge pixels and hard edge pixels. This groping have been provided the opportunity to manipulate different payload with different pixel group. FIS have been optimized the edge embedding technique. These ideas have been verified through parameters. In addition Arithmetic Lossless compression method has supported the higher pay load and 100% of secret data recovery. [1]. Petitcolas, F. A. P., R. J. Anderson, and M. G. Kuhn. "Information Hiding - a Survey." Proceedings of the IEEE, vol. 87, no. 7, 1999, pp , SCOPUS, doi: / [2]. Carvajal-Gamez, B. E., F. J. Gallegos-Funes, and A. J. Rosales-Silva. "Color Local Complexity Estimation Based Steganographic (CLCES) Method." Expert Systems with Applications, vol. 40, no. 4, 2013, pp , SCOPUS, doi: /j.eswa [3]. Uma Maheswari, S., and D. Jude Hemanth. "Performance Enhanced Image Steganography Systems using Transforms and Optimization Techniques." Multimedia Tools and Applications, vol. 76, no. 1, 2017, pp , SCOPUS, doi: /s [4]. Cheddad, A., et al. "Digital Image Steganography: Survey and Analysis of Current Methods." Signal Processing, vol. 90, no. 3, 2010, pp , SCOPUS, doi: /j.sigpro [5]. S.Abinaya G.Arulkumaran DETECTING BLACK HOLE ATTACK USING FUZZY TRUST APPROACH IN MANET International Journal of Innovations in Scientific and Engineering Research (IJISER),vol4,no3,pp ,

7 [6]. Sahu, A. K., and G. Swain. "A Review on LSB Substitution and PVD Based Image Steganography Techniques." Indonesian Journal of Electrical Engineering and Computer Science, vol. 2, no. 3, 2016, pp , SCOPUS, doi: /ijeecs.v2.i3.pp [7]. Shaik, A., V. Thanikaiselvan, and R. Amitharajan. "Data Security through Data Hiding in Images: A Review." Journal of Artificial Intelligence, vol. 10, no. 1, 2017, pp. 1-21, SCOPUS, doi: /jai [8]. Duric, Z., M. Jacobs, and S. Jajodia. Information Hiding: Steganography and Steganalysis, vol. 24, 2004, SCOPUS,, doi: /s (04) [9]. Amirtharajan, R., V. M. Abhiram, G. Revathi, B. J. Reddy, V. Thanikaiselvan, and M. B. B. Rayappan "Rubik's Cube: A Way for Random Image Steganography." Research Journal of Information Technology 5 (3): doi: /rjit

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