An Efficient Neural Network based Algorithm of Steganography for image

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1 An Efficient Neural Network based Algorithm of Steganography for image Imran Khan Abstract To provide large capacity of the hidden secret data and to maintain a good visual quality of stego-image a novel steganographic method based on neural network and random selection of edged areas of pixels is proposed in this research paper. Firstly a cover image is divided into a non-overlapping two pixels block and this pixel block generates a set of edged non-overlapping regions. After this a neural network is applied which generates a stego-image which has been immune against conventional attack and performs good perceptibility compared to other steganographic approaches. From our experimental results it can be shown that the proposed method hides information in edged regions and maintains a better visual display of stego image than the traditional methods. Index Terms: Coverimage, LSB, Stego-image, and Steganography. I. INTRODUCTION With advancements in digital communication technology and the growth of computer power and storage, the difficulties in ensuring individual s privacy become increasingly challenging. The degrees to which individuals appreciate privacy differ from one person to another. Various methods have been investigated and developed to protect personal privacy. Encryption is probably the most obvious one, and then comes steganography. Encryption lends itself to noise and is generally observed while steganography is not observable. Steganographic techniques [1] have been used for centuries and being applied across a broad set of different digital technologies. It has been observed that terrorists have been utilizing steganographic techniques within digital images to communicate to the internet [2]. A. Implementing steganography Secrets can be hidden inside all sorts of cover information: text, images, audio, video and more. However, there are tools available to store secrets inside almost any type of cover source. A possible way of storing a secret inside a text is using a publicly available cover source, a book or a newspaper, and using a code which consists for example of a combination of a page number, a line number and a character number [2]. Hiding information inside images is a popular technique nowadays. An image with a secret message inside can easily be spread over the World Wide Web or in the newsgroups. The use of steganography in newsgroups has been researched by German steganographic expert, who created a scanning cluster which detects the presence of hidden messages inside images that were posted on the internet. To hide a message inside an image without changing its visible properties, the cover source can be altered in noisy areas with many color variations, so less attention will be drawn to the modifications. The most common methods to make these alterations involve the usage of the least-significant bit or LSB, masking, filtering and transformations on the cover image. The LSB method [3] uses bits of each pixel in the image. When using a 24 bit color image, a bit of each of the red, green and blue color components can be used, so a total of 3 bits can be stored in each pixel. Masking and filtering techniques usually restricted to 24 bits or grayscale images. These methods are effectively similar to paper watermarks, creating markings in an image. This can be achieved for example by modifying the luminance of parts of the image. While masking does change the visible properties of an image, it can be done in such a way that the human eye will not notice the anomalies. Least Significant Bit maintains a good visual quality of stego-image, it can hide little information. Considering the drawback of LSB, some methods begin to take account of the visual identity that human eyes are insensitive to edged and textured areas when embedding secret information, such as BPCS(biplane complexity segmentation)[4],pvd(pixel value differencing[5]), MBNS (multiple base notational system [6]), SOC, Side Match [7] and WCL.The capacity of embedded information is thereby greatly improved while the quality of visual imperceptibility is maintained. As human vision sensitivity is complex, it is hard to exactly decide whether a pixel is in less sensitivity areas or not. Thus, based on the contrast and texture sensitivity, we train self-organizing map Neural Networks (NNs) trained to distinguish pixels in less sensitive areas from pixels in more sensitive areas. So, NNs trained is the secret key. Then, we use NNs trained to classify pixels, and select pixels in less sensitive areas to embed more secret data. On the receiving side, the original image is not needed for extracting the embedded data. Basic elements of steganography in images are shown in Figure 1. The carrier image in steganography is called the "cover image" and the image which has the embedded data is called the "stego image". The embedding process is usually controlled using a secret key shared between the communicating parties. 63

2 This key ensures that only recipients who know the corresponding key will be able to extract the message from a stego image. Sabeti et. al [14] have successfully attacked basic PVD and the enhanced version of PVD. Table 1: Example of an image Table 2: Vectorized image Fig 1. Typical element of any steganographic system II. PIXEL VALUE DIFFERENCING METHOD A popular digital steganography technique is the so-called least significant bit (LSB) replacement. With the LSB replacement technique, the two parties in communication share a private secret key that key that creates a random sequence of samples of a digital signal. The encrypted secret message is embedded in the LSB's of those samples of the sequence. This digital steganography technique takes the advantage of random noise present in the acquired images [8]. Many reliable steganalytic methods have been devised for LSB flipping technique. The production of Pair of Value (PoV) in the histogram of a stego image is the main weak point of the LSB flipping. The presence of PoV has allowed many steganalysis methods, such as RS to successfully attack LSB flipping[10,11]. Wu and Tsai [12] presented a steganographic method based on Pixel Value Differencing (PVD). They divide the cover image into a number of non- overlapping two-pixel blocks. Each block is categorized according to the difference of the gray values of the two pixels in the block. A small difference value indicates that the block is in a smooth area and a large one indicates that it is in an edged area. The pixels in edged areas may tolerate larger changes of pixel values than those in the smooth areas. Therefore, it is possible to embed more data in edged areas than in the smooth areas. All possible difference values are classified into a number of ranges. The number of bits that are to be embedded in a pixel pair depends on the width of the range that the difference value belongs to. PVD is immune against the attacks that scrutinize changes in spatial domain [11] or changes in the histogram [10]. In [13] another method based on PVD is proposed which tries to increase the embedding capacity of PVD. In this version the image blocks are categorized based on the calculated pixel differences into two groups of smooth and edge. A block with difference less than a threshold is a smooth block; otherwise, it is an edge block. LSB embedding is used for smooth regions and PVD embedding for edged areas. We refer to this method as the enhanced PVD approach as opposed to the basic PVD approach discussed in [10]. The cover images used in the PVD method are supposed to be 256 gray-valued ones. A difference value d is computed from every non-overlapping block of two consecutive pixels, say p i and p i+1 of a given cover image. Partitioning the cover image into two-pixel blocks runs through all the rows of each image in a zigzag manner. In Figure 2, an example of an image is shown. The two-pixel blocks that are constructed by zigzag scanning of the example image are shown in Figure 3. Assume that the gray values of p i and p i+1 are g i and g i+1, respectively; then d is computed as g i+1 -g i which may be a number ranging from -255 to 255.A block with d close to 0 is considered to be an extremely smooth block, where as a block with d close to -255 or 255 is considered as a sharply edged block. The method only considers the absolute values of d (0 through 255) and classifies them into number of contiguous ranges, such as R k where k = 1, 2,, q by l k and u k, respectively, where l 1 is 0 and u q is 255. The width of R k (w k ) is u k - l k +1. In PVD method, the width of each interval is taken to be a power of 2.A practical set of intervals may be: [0; 7]; [8; 15]; [16; 31]; [32; 63]; [64; 127]; [128; 255].Every bit in the bit stream should be embedded into the two-pixel blocks of the cover image. Given a two-pixel block B with gray value difference d belonging to k th range, then n bits can be embedded in this block, and can be calculated by n = log(u k + l k + 1) which is an integer. A sub-stream S with n bits is selected next from the secret message for embedding in B. A new difference d then is computed by: Here b is the value of the sub-stream S. Because the value b is in the range from 0 to u k - l k, the value of d is in the range from l k to u k. If we replaced d with d, the resulting changes are presumably unnoticeable to the observer. Then b can be embedded into pixels p i and p i+1 in a manner that the new pixel values produce a difference of d0. 64

3 The new gray values (g i, g i+1 ) are obtained for the pixels in the to the corresponding two-pixel block (p I,p i+1 ) of the stego-image. The embedding process is finished when all the bits of the secret message are embedded. The calculation for computing (g i, g i+1 ) from the original gray values (g i, g i+1 ) of the pixel pair is based on a function f((g i, g i+1 ),m) which is defined to be Once network weights are converged to certain values, proposed method use these values and the coordinates of selected feature sub blocks as extraction keys. This extraction keys must be shared among the embedder and the extractor in order to extract proper hidden signals from the contents. First, frequency transformation of the image is performed. Same amount of unique sub blocks as number of classification patterns must be chosen from the cover image. Sufficient number of neural networks must be prepared, which will be the number of binary digits to satisfy the classification patterns. Where m is d - d. obviously embedding is only considered for pixels whose new pixel values would fall in the range of [0,255]. III. PROPOSED METHOD In this section, we explain how proposed method hides information in stego-image and how we retrieve information from the stego-image. With our method, the use of neural network is the key technique. Firstly a cover image is divided into a non-overlapping two pixels block and these pixel block generates a set of edged non-overlapping regions.all these blocks are generated by using PVD differencing method.now,the embedder adjusts a neural network weights with desired hidden bit code from the collection of both cover image and stego-image folder(i.e. our training data). This pattern is common for both embedding and extracting the hidden information. For learning we can use supervised learning of the neural network. By using XOR neural network learning model we embed the secret message within a cover image. At the first pixel location of stego image we embedded the message length and the edged pixel maps of a cover image A. Information hiding algorithm In the information hiding algorithm firstly we generated a set of those pixels which are highly suited for steganography.these pixels are generated by taking the pixel difference of two adjacent pixel block.this technique is described in section 2.Now in training, one must decide the structure of neural network. The amount of units for input layer is decided by the number of pixels selected from set of edged region pixel map. In proposed method, the feature values are diagonal coefficient values from frequency transformed selected feature sub blocks. For better approximation, one bias neuron is added for input layer. The neural network is trained to output a value of 1 or 0 as an output signal. In proposed method, one network represents one binary digit for corresponding secret codes. The adequate amount of neurons in the hidden layer, for back propagation learning in general, is not known. So the number of neurons in hidden layer will be taken at will. In proposed method, six hidden units are used. For better approximation, one bias neuron like is introduced for hidden layer as well. Fig 2. Flow Chart of Proposed Method In case for 32 input patterns, five networks are enough to represent 32 different identification values because five binary digits are sufficient to distinguish for 32 patterns. Learning of all networks is repeated until the output value satisfies a certain learning threshold value. After all network weights are converged, the coordinates of sub blocks and the values of network weights are saved. Extractor will use this information to extract hidden codes in the extraction process. B. Extraction algorithm Extraction process starts from the reading of stego-image and a extraction key information. After providing this information we calculate the total length of the message which will embed with a stego-image. We also retrieve the information of edged region pixel set from stego-image. Only by knowing the proper coordinates of feature sub blocks will lead user to the proper input values. By knowing proper network weights, extractor can induce the structure of the network and only proper network weights are able to output 65

4 the proper hidden codes. After constructing the neural network, extractor examines the output value from the network with the input values induced from the feature sub blocks. This procedure is shown in Figure 4. Each network output either 1 or 0 with the aid of threshold for output unit. The flow chart for calculation of extraction is given as Fig 3. Flow Chart for Calculation of extraction algorithm IV. IMPLEMENTATION AND MEASURES Dot net platform is chosen to develop the above steganographic algorithm. In a Dot net framework there are extensive libraries and efficient functions of neural network and image processing which is very useful in steganography. Developers may use other programming language also.security, embedding distortion and embedding rate can be used as schemes to evaluate the performance of the data hiding schemes. A. Entropy A steganographic system is perfectly secure when the statistics of the cover data and the stego data are identical, which means that the relative entropy between the cover data and the stego-data is zero. Entropy considers the information to be modeled as a probabilistic process that can be measured in a manner that agrees with intuition [14].The information theory approach to steganography holds the systems capacity to be modeled as the ability to transfer information. More information regarding information theory and its application to steganography can be found at [14]. B. Mean Squared Error & SNR The (weighted) mean squared error between the cover image and the stego-image (embedding distortion) can be used as one of the measures to assess the relative perceptibility of the embedded text. Imperceptibility takes advantage of human psycho visual redundancy, which is very difficult to quantify. Mean square error (MSE) and Peak Signal to Noise Ratio (PSNR) can also be used as metrics to measure the degree of imperceptibility. MSE= [ M i=1 N j=1 ( f ij g ij ) 2 ] MN PSNR=10 log 10 (L 2 / MSE) f ij is the pixel value from the cover image, g ij is the pixel value from the stego-image, and L is the peak signal value of the cover image (for 8-bit images, L=255). Signal to noise ratio quantifies the imperceptibility, by regarding the message as the signal and the message as the noise. Thus, the higher the SNR, the more perceptible is the message. SNR = 2 S / 2 N C. Correlation Correlation is one of the best known methods that evaluate the degree of closeness between two functions. This measure can be used to determine the extent to which the original image and the stego-image are close to each other, even after embedding data Localization, that is detection of the presence of the hidden data relies on the use of cross correlation function R XY of two images X and Y, defined as[ 8], R XY (, ) = i j X(i,y) Y(i-,j- ) D. Ensuring Integrity-using Checksums In order to ensure the integrity of data and the cover medium, mechanisms should be employed that either detect that the medium has been altered or is able to withstand such changes and corrects them to the original state. Checksums could be used to alert the user of possible contamination or tampering. For monochrome images the application of checksums is going to straightforward with the checksums being calculated for the appropriate number of bits required to represent each of the pixels. For color images, the checksum scheme can be extended three times to the three-color planes. The checksum could also be calculated in a new coordinate system, for e.g., hue saturation intensity plane instead of RGB plane, and the resulting checksum could be embedded in the original coordinate plane. V. RESULT ANALYSIS In this experiment we used PNG and BMP images of various resolutions as cover image. We train a set of 150 images which is randomly taken from internet. These images have various memory sizes. Table 3: Dataset Taken for consideration proposed method LSB Steganography COVER IMAGE PSNR PSNR Federar.bmp Beach.bmp lily.bmp LENA.BMP PEPPERS.PNG FLOWER.PNG

5 By performing the comparison of the cover image and the stego-image of above two methods datasets we come to conclude that the perceptibility ratio of the proposed method is better than the LSB steganography technique. Fig.4.Performance analysis in terms of PSNR ratio VI. CONCLUSION AND FUTURE WORK Given the high degree of redundancy present in a digital representation of image, there has been an increased interest in using it for the purpose of steganography. The paper suggested how a selection of different portions of the images increased the chances of hiding data in a more effective way. This type of embedding also reduces the chances of detection of secret message. Variation of the LSB [8] insertion algorithm. This algorithm can be used for achieving better security and also improved covertness. However for the future work of this method, we recommend the secret message should be compressed or encoded before the hiding process takes place. This is important because in this way we will minimize the amount of information that is sent, and hence minimizing the chance of degrading the image. At the same time results of steganalysis can be used to change or improve embedding techniques. REFERENCES [1] Liu Shaohui, Yao Hongxun, Gao Wen Neural Network based Steganalysis in Still Images Proceedings of IEEE ICME [2] I.Aveibas, N.Memon, B.Sankur. Steganalysis based on image quality metrics. Multimedia Signal Processing, 2001 IEEE Fourth Workshop on, [3] ZNeil F.Johnson and Sushhil Jajodia. Steganalysis: The Investigation of Hidden Information. Proceedings of the IEEE Information Technology Conference, Syracuse, New York, USA [4] R.Chandramouli and N. Memon, Analysis of LSB based image steganography techniques, in Proc. ICIP, Oct [5] Chandramouli, M. Kharrazzi, and N. Memon Image steganography and steganalysis: Concepts and Practice,Springer-Verlag, vol. 2939, [6] Jessica Fridrich, Miroslav Goljan. Practical Steganalysis of Digital Images-State of the Art. [7] N.Provos and Peter Honeyman, Detecting Steganographic content on the internet,citi Technical Report 01-11, [8] N.Provos, Defending against statistical analysis, 10th USENIX Security Symposium,Washington,DC,August [9] Xuefeng Wang and Yingjun Feng. A New Learning Algorithm for Neural Networks. Journal of Harbi institute of technology. 29(2): [10] Xuefeng Ying & jun Feng. A Fast Learning Algorithm of Multi-Layer Neural Network. OR Transactions. 5-9, [11] B. Chen and Wornell. Implementations of quantization index modulation methods for digita watermarking and information embedding of multimedia. Special Issue on Multimedia Signal Processing, vol. 27:7-33, [12] Voloshynovskiy, S.,Herrigel and Rytsar Y. StegoWall: Blind statistical detection of hidden data. Proceedings of SPIE. Vol. 4675: [13] Shadrokh samavi,vajiheh Sabeti,Mojtaba Mahadevi and Shahram Shirani. Steganalysis of embedding in difference of image pixel pairs by neural network. Journal of ISeCure, volume 1, pages 17-26, January [14] Kensuke Naoe,Yoshiyasu Takefuji. Damageless Information Hiding using Neural Network on YCbCr Domain. IJCSNS Journal,volume 8,pages 81-86,september [15] Mehdi Kharrazi,Husrev T. Sencar & Nasir Memon, Performance study of common image steganography and steganalysis techniques.ieee Journal of Electronic Imaging 15(4), (Oct Dec 2006). [16] WangYan & Ling-di Ping. A New Steganography Algorithm Base on Spatial Domain. Second International Symposium on Information 1/09 IEEE. [17] Zhang Jiajia, Liu Jing & Deng Cheng1.A Steganographic Method based on SOM and wavelet contrast. IEEE International Conference on Artificial Intelligence and Computational Intelligence /09 IEEE. [18] Sambasiva Rao, Baragada S. Ramakrishna,M. S. Rao & S. Purushothaman. Polynomial Discriminant Radial Basis Function for Steganalysis. IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.2,pages , February [19] Asha, A. Information hiding - The Art of Steganography, GSEC practical. SANS Institute, AUTHOR BIOGRAPHY Imran Khan received his Bachelor s Degree in Computer Science & Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal (M.P.)-India in year 2005 and M. Tech. (Information Technology) from Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal (M.P.)-India in year He is an Assistant Professor in Oriental Institute of Science and Technology, Bhopal in the Department of Information Technology. His research areas are neural network, artificial intelligence and network security. He is having six research papers in International and national Journals 67

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