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2018 International Conference on Information and Communications Technology (ICOIACT) An Improved Message Capacity and Security using Divide and Modulus Function in Spatial Domain Steganography De Rosal Ignatius Moses Setiadi, Heru Agus Santoso, Eko Hari Rachmawanto, Christy Atika Sari Department of Informatics Engineering, Computer Science Faculty Dian Nuswantoro University Semarang, Indonesia Email: moses@dsn.dinus.ac.id; heru.agus.santoso@dsn.dinus.ac.id; eko.hari@dsn.dinus.ac.id; atika.sari@dsn.dinus.ac.id Abstract Image Steganography is a technique for hiding messages into digital images, so messages cannot be perceived by the human senses. The two most important aspects of steganography techniques are the payload capacity and imperceptibility of embedded messages. This research proposes a method to explode secret messages using divide and modulus functions so that the message capacity embedded in a digital image can increase. The divide and modulus function can also improve message security because the messages are split into two part and sent separately. One part embedded and the other is stored as a key extraction. This method is done in a spatial domain. Spatial domains are steganographic techniques performed by manipulating pixel values directly. LSB is one of the most popular spatial domains proposed in this research. To measure the quality of imperceptibility are used PSNR and MSE. Based on the experimental results of this study proved that the capacity of embedded messages can increase twofold and keep maintaining the imperceptibility quality. Keywords Image Steganography, Modulus Function, Spatial Domain, LSB, Payload Capacity I. INTRODUCTION Steganography is one of the popular techniques for securing messages sent through the internet media. The Internet is a giant network that can be accessed by everyone in the world. This allows theft of messages to be sent. This is the main reason why the steganographic techniques must be developed [1]. Steganography is done by hiding the message in a container media or cover media. There are various covers that are used such as text, audio, image, and video [2]. The image is one of the steganography media are widely studied and will be discussed in this study. The digital imager is visual data that can be perceived by the human vision, then a good steganography on the image should not be perceived by the human eyes directly [3]. This is called the imperceptibility aspect, which is the most important aspect in determining the quality of the steganography method [4]. Imperceptibility is strongly influenced by the message capacity embedded in the cover. The greater the capacity of the embedded message then the quality of imperceptibility decreases. Therefore, many steganographic studies suggest ways to increase message capacity while maintaining imperceptibility. The Least Significant Bit (LSB), is a popular method in spatial domain steganography [5]. LSB is a steganography technique that makes the perceptual message invisible but has a simple computational complexity [6]. In several studies like [6], [7], [8], [9] LSB techniques combined with modulo functionality to improve message embedding capacity [10], and even increase resistance to statistical analysis and histogram. So in this study also proposed the use of the function of the modulus and division functions to optimize the algorithm to obtain a technique that has a larger payload, more secure and keep it simple in computational complexity. II. RELATED WORK Sun et al [8] integrate the LSB technique and the Exploiting Modification Direction (EMD) system with the goal of augmenting capacity and quality of stego imagery. The EMD system is modified by adding modulo functionality. The experimental results obtained on this method are significantly increased message capacity but there is a slight decrease in the quality of PSNR when compared to traditional EMD systems. Vijayalakshmi et al [6], combining the LSB method with module function and pseudo-random index generator with the key on RGB or grayscale image. Further testing is done statistical analysis and histogram. Statistical analysis is measured by Mean, Variance, RMS, and color difference in color images. Test results show this method does not change the value significantly and improve the security of hidden data. Nagaraj et al [7], also proposed a combination of LSB methods and modulo functions. The image tested in the method is an RGB image of 9067 bytes. While the inserted message measuring 1 kilobytes to 5 kilobytes. Further stego image is done statistic analysis and histogram. Based on experimental results showed no significant change in the value of statistics and histogram. It's just that the image looks blur when the message of 5 kilobytes pinned. This is quite reasonable because the message capacity is relatively large. Akhtar et al [9], also proposed a combination of LSB substitution methods and modulo functions. It's just modulo function used to compress messages. Before the message is embedded, the message is divided into two sections. 978-1-5386-0954-5/18/$31.00 2018 IEEE 186

2018 International Conference on Information and Communications Technology (ICOIACT) Furthermore, both components are embedded into the cover image using modulo function. The results of this study show that there is increased capacity and message security while maintaining imperceptibility quality of stego image. The quality of imperceptibility is measured by PSNR, where the resulting PSNR value range is 34-40dB. Based on previous research, this research proposed LSB combination and modulo function and division function to insert a secret message on the cover image. The secret message will be split into two parts with the division and modulus functions. One part is embedded and another part is sent as the extraction key. This is done for capacity building, security and stego image quality. III. A. Embedding Scheme Message Image PROPOSED METHOD Reshape to one dimension vector cover image. The second part is the mod data which is stored as the key of the extraction. (1) (2) Where: = div data =mod data =message in vector form 3. Convert the div data into binary form. 4. Read the cover image then change to binary form. 5. Embed the div data on the smallest bit of cover image. 6. Replace the cover image with the bit form to the pixel value, so we get the stego image. Note that the division and modulus functions both use constants 16. This number is chosen to divide a pixel value of 8 bits into two-valued data every 4 bits. B. Extracting Scheme LSB Extraction Divide and Mod Fuctions Stego Image Div data Cover Image Div data Save Mod data for Extraction Key Mod data (key) Reshape to size of message image Embed using LSB Joint formula Fig. 1. Proposed Embedding Scheme Stego Image The insertion scheme is one of two major steganographic processes. Figure 1 is the proposed insertion scheme. The following is a description of the insertion scheme based on Figure 1. 1. Read the message image then change it to a onedimensional vector form. 2. Perform the divide function and modulus on the vector of the message image in a loop. Use the formula (1) the divide and formula (2) function for the modulus function, so the message is divided into two parts. The first part is the div data which will be inserted into the Fig. 2. Proposed Extracting Scheme Recover Message Image Extraction scheme is a scheme to get back the embedded message on the cover image. Figure 2 describes the steps of the proposed extraction scheme. Here are the details of the steps shown in Figure 2. 1. Read stego image then do the LSB technique to get the data div. 2. Reshape div data into the size of the cover image. 3. Combine div data and mod data with join function with formula (3). (3) 187

2018 International Conference on Information and Communications Technology (ICOIACT) 4. Get the extraction of the message image. IV. EXPERIMENTAL RESULT AND ANALYSIS In this research used six standard cover image with size 256*256 as the cover image. The standard image is used to simplify the process of comparing with other studies as well as subsequent research. The cover image used is a grayscale type with three types of extensions: tiff, jpg, and bmp, cover image can be seen in figure 3. imperceptibility with PSNR and MSE. The stego image of the embedding can be seen in Figure 5. (a) (b) (a) (b) (c) (d) (c) (d) (e) (f) Fig. 5. Stego images{(a)clock.tiff, (b)couple.tiff, (c)cameraman.bmp, (d)chart.tiff, (e)goldhill.bmp, (f)livingroom.jpg} (e) Fig. 3. Cover images used {(a)clock.tiff, (b)couple.tiff, (c)cameraman.bmp, (d)chart.tiff, (e)goldhill.bmp, (f)livingroom.jpg} Fig. 4. Message image used (lena.bmp) The messages used in this study use grayscale image with size 128 * 128. Image of the message used can be seen in figure 4. This message can also be replaced text or other media with a size of 16384 bytes, or equivalent to 131072 bits. The message is then embedded in the cover image. The result of embedding is called stego image and then measured the quality of (f) The message embedded on the cover image is relatively large enough that 25% of the cover image. However, based on figure 5 it can be concluded that there is no significant change in pixel values visible to the human eye. Then PSNR and MSE are used to measure the quality of imperceptibility. The value of PSNR and MSE is obtained by comparing the cover image with stego image. The higher the PSNR value the better the quality, otherwise if the value of MSE is greater then the quality is getting down [11]. The quality of good PSNR is at least more than or equal to 40 db. Formula 4 is used to calculate the PSNR value while formula 5 is used to calculate the MSE value. (4) (5) Where :, is size of image; is cover image; 188

2018 International Conference on Information and Communications Technology (ICOIACT) is stego image. Table 1 below summarizes the results of calculations based on formula (4) and formula (5). TABLE I. IMPERCEPTIBILITY QUALITY BASED ON PSNR AND MSE Images MSE PSNR clock.tiff 0.2475 54.1948 couple.tiff 0.0597 60.3699 cameraman.bmp 0.2474 54.1967 chart.tiff 0.0333 62.9031 (a) Fig. 9. Histogram analisis of couple.tiff {(a) histogram of cover image, (b) histogram stego image) (b) goldhill.bmp 0.2430 54.2756 livingroom.jpg 0.1185 57.3953 Average 0.1582 57.2226 Based on table 1 can be seen that the value of PSNR quite varied, with a range of values 54.1948 db to 62.9031 db. Similarly, the value of MSE has a range of values 0.0597 to 0.2475. The results are shown in Table 1 indicate that the image quality of stego rated relatively excellent. (a) Fig. 10. Histogram analisis of goldhill.bmp {(a) histogram of cover image, (b) histogram stego image) (b) (a) Fig. 6. Histogram analisis of cameraman.bmp {(a) histogram of cover image, (b) histogram stego image) (a) Fig. 7. Histogram analisis of chart.tiff {(a) histogram of cover image, (b) histogram stego image) (a) Fig. 8. Histogram analisis of clock.tiff {(a) histogram of cover image, (b) histogram stego image) (b) (b) (b) (a) Fig. 11. Histogram analysis of livingroom.jpg {(a) histogram of cover image, (b) histogram stego image) Histogram analysis is also done to observe the number of pixel values that change. By observing carefully there is a slight difference on the histogram of the cover image and stego image, this is quite visible visually in figure 6, figure 8, figure 10. While in figure 7, figure 9, and figure 11 there is almost no difference in the histogram. The next test process is the measurement of message extraction results. Messages should be perfectly extracted so that the information sent is actually delivered. To measure the result of message extraction is used Correlation Coefficient (cc) by using formula (6). Based on the extraction test results obtained value 1 on all stego images, this proves the message extracted perfectly. (b) Where, is sum of pixels value of message image is original message image is extraction message image (6) 189

2018 International Conference on Information and Communications Technology (ICOIACT) V. CONCLUSION Based on the results of hypotheses and experiments that have been done in this study proved that the use of divide and modulus operations can increase the payload of messages by two-fold and message security also increased. This is because the embedded message will be divided into two parts with the divide and modulus functions. The two parts are data mod and div data, div data pinned and mod data are sent separately. The quality of imperceptibility is very satisfactory with an average PSNR value of 57.2226 db and an average value of MSE 0.1582. Messages can also be extracted perfectly, which has obtained a value of CC 1 for all stego images. REFERENCES [1] E. H. Rachmawanto, R. S. Amin, D. R. I. M. Setiadi dan C. A. Sari, A Performance Analysis StegoCrypt Algorithm based on LSB-AES 128 bit in Various Image Size, in International Seminar on Technology for Technology of Information and Communication (isemantic), Semarang, 2017. [2] K. Joshi, P. Dhankhar dan R. Yadav, A New Image Steganography Method in Spatial Domain Using XOR, in Annual IEEE India Conference (INDICON), New Delhi, 2015. [3] Z. Y. Al-Omari dan A. T. Al-Taani, Secure LSB Steganography for Colored Images using Character-Color Mapping, in International Conference on Information and Communication Systems (ICICS), Irbid, 2017. [4] M. Najih, D. R. I. M. Setiadi, E. H. Rachmawanto, C. A. Sari dan S. Astuti, An Improved Secure Image Hiding Technique Using PN- Sequence Based On DCT-OTP, in International Conference on Informatics and Computational Sciences (ICICoS), Semarang, 2017. [5] C. Irawan, D. R. I. M. Setiadi, C. A. Sari dan E. H. Rachmawanto, Hiding and Securing Message on Edge Areas of Image using LSB Steganography and OTP Encryption, in International Conference on Informatics and Computational Sciences (ICICoS), Semarang, 2017. [6] V. Vijayalakshmi, G. Zayaraz dan V. Nagaraj, A Modulo Based LSB Steganography Method, in International Conference on Control, Automation, Communication, and Energy Conservation (INCACEC), Perundurai, 2009. [7] V. Nagaraj, V. Vijayalakshmi dan G. Zayaraz, Modulo Based Image Steganography Technique against Statistical and Histogram Analysis, International Journal of Computer Applications (IJCA), vol. 4, no. Network Security and Cryptography, pp. 34-39, 2011. [8] H.-M. Sun, C.-Y. Weng dan S.-J. Wang, A Scheme of Modulo-Based Capacity-improvement upon EMD Systems, in International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH- MSP), Kyoto, 2009. [9] N. Akhtar, V. Ahamad dan H. Javed, A Compressed LSB Steganography Method, in International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, 2017. [10] M. Sabokdast dan M. Mohammadi, A Steganographic Method for Images with Modulus Function and Modified LSB Replacement based on PVD, in Conference on Information and Knowledge Technology (IKT), Shiraz, 2013. [11] A. Winarno, D. R. I. M. Setiadi, A. A. Arrasyid, C. A. Sari dan E. H. Rachmawanto, Image Watermarking using Low Wavelet Subband based on 8 8 Sub-block DCT, in International Seminar on Application for Technology of Information and Communication (isemantic), Semarang, 2017. 190