Multilayer Data Embedding Using Reduced Difference Expansion

Multilayer Data Embedding Using Reduced Difference Expansion DINESH SATRE 1, DEVYANI BONDE 2, SUBHASH RATHOD 3 Department Of Computer Engineering Marathwada Mitra Mandal s Institute of Technology Savitribai Phule Pune University Pune, Maharashtra INDIA 1 dbsatre@gmail.com, 2 divyabonde@gmail.com 3 subhashrathod@gmail.com Abstract: - There are two basic applications of image data embedding, steganography and watermarking. The first one requires high embedding capacity and other one requires cover image will get revert to original. Both of these task is performed by the very popular algorithm proposed by Tian[7], called Difference Expansion. For multilayer operation, the proposed algorithm results in the poor visual quality of an image. The visual quality of an image can be improved by reducing the difference value. This paper proposes the image pre-processing technique so that after applying the difference expansion algorithm, the resulting image will be nearly same as the original image. Key-Words: - Image Data Embedding, Difference Expansion, Steganography, Watermarking, Multilayer Data Embedding. 1 Introduction The multilayer data embedding is the process of image data embedding in which the embedding algorithm is applied repeatedly so that more and more data will get embedded into an image without degrading the quality of image. If think about the application point of view, the multilayer data embedding is used for covert communication in which data embedding capacity should be high. For image data hiding, the performance of a data embedding algorithm is measured by the visual quality of the embedded image and the hiding capacity of an image. Therefore, researchers on data embedding aim at developing low distortion and high embedding capacity methods. Many of the researchers propose the reversible and distortionless data embedding techniques. It is also called as lossless data embedding or distortionfree data embedding [2]. With the distortion free property, reversible data embedding can be used to hide information into sensitive images such as military images or medical images. A novel distortionless image data hiding algorithm based on integer wavelet transform [5] that can invert the stego image into the original image without any distortion after the hidden data are extracted. The reversible data embedding using patchwork algorithm is devised by Macq[1] for robust watermark. But the technique is suffered from a salt and pepper noise. Vleeschouwer, et al[6] reduce the salt and pepper noise by using a circular interpretation of bijective transformation. This method is based on modulo-256 addition to achieve losslessness that may cause the salt and pepper noise. A new robust lossless data hiding technology is developed by Z. Ni, et al. s [9] that does not use modulo-256 addition. The image quality of Z. Ni method is improved by slight modification in algorithm [10]. Fridrich, et al [2] also devised a reversible watermark that does not suffer from a salt and pepper noise. Celik, et al[4] enhance Fridrich approach an devised a low distortion, high capacity data embedding algorithm. In 2002, Tian[7] used the difference expansion to design reversible data embedding algorithm. The algorithm can be used for multilayer data embedding to increase the embedding capacity. But, by increasing the embedding capacity, the visual quality of an image is also get degraded. The generalized version of DE method was also proposed by Alattar [8]. The rest of the paper is organized in section 2 to section 5. Section 2 gives the brief introduction about the Difference Expansion technique in image data embedding. The proposed method is presented in section 3. The experimental results including PSNR values are presented in section 4. Finally, section 5 describes the conclusion of the paper. ISBN: 978-1-61804-344-3 202

2 Difference Expansion Difference expansion is the most popular method for the reversible data embedding with the feature of high embedding capacity. It works on finding the difference and the average of a pair of pixels. The difference value is used for embedding the data. The working to the algorithm is explained as follows: Consider a grayscale pixel pair (x, y), where x=141 and y=64. If the embedding bit b=1, then integer average l and difference h of pixel pair is calculated as where,. x + y 141+ 64 l = = = 103 2 2 h = x y = 141 64 = 77 is the floor function. From these two values, only the difference h is the eligible candidate for data embedding. For this the difference value h is converted into binary number as h = 77 = (1001101) b. Then embedding bit b=1 is appended at LSB of difference value h as h= (10011011) b = (155) d. Mathematically, this is equivalent to h ' = 2 h + b = 2 77 + 1 = 155 Finally, new pixel values are calculated as h' + 1 155 + 1 x' = l + = 103 + = 181 2 2 h' 155 y' = l = 103 = 25 The original pixel (x, y) is retrieved from these two embedded pixel by following the same procedure and extracting the LSB of difference value. To prevent overflow and underflow h should satisfy h' 2 255 l and h ' 2l + 1. ( ) 3 Proposed Method The proposed method is based on the idea to get the nearly original image after bit embedding. Therefore, the cover image has to be preprocessed for data embedding. The basic idea of preprocessing is based on the observation of embedding process using difference expansion. In difference expansion, the LSB bit of difference value is used for embedding. By adding the LSB bit, the difference value will get double as defined by h ' = 2 h + b In image pre-processing, the difference value h is obtained such that it will be nearly equal to h. This can be done as follows where, h r h' = 2 hr + b h =, therefore we get 2 h ' = h + b For b = 0, h = h and for b = 1, h = h+1. Now, again consider the pixel pair (x, y), where x=141 and y=64. With these values the difference h = 77 and average l = 103. For the image preprocessing, the h r should be calculated as h r 77 = h = = 39 2 With this difference value the preprocessed pixel pair (x p, y p ) is calculated as x p y p h 77 = x = 141 = 122 4 4 h 77 = y + = 64 + = 83 4 4 ISBN: 978-1-61804-344-3 203

With this new pixel pair ( xp, y p ), apply the difference expansion algorithm and find out the integer average l and difference value h. 122 + 83 l = = 103 2 h = 122 83 = 39 If the embedding bit b = 0, embedded pair (x, y ) is obtained as x =141 and y = 63. If the embedding bit b = 1, embedded pixels are obtained as x =142 and y =63. Observe both the result with bit b=0 and b=1, the embedded pixel pair is nearly equal to the original pixel pair. Obviously, the visual quality of an image will get increased and multilayer data embedding is possible with very less degradation of original image. Fig.1 shows the 8 x 8 image block of Baboon, it is considered as the original image. After applying the embedded algorithm, the embedded block is obtained as shown in fig.2. By observing these two image blocks, it can be concluded that there are very slight changes in the pixel values of an image. Therefore, the visual quality of an embedded image retains high. Because the difference expansion algorithm is reversible, the cover image is recovered from the embedded image as shown in fig.3. In fig. 3, some pixels are not recovered, highlighted as bold and color. To overcome this problem, location map should be prepared to identify whether the pixel pairs are embeddable or not. 141 64 58 92 134 94 69 45 116 103 50 74 94 63 72 55 82 114 56 56 100 67 67 57 77 133 98 57 87 119 62 56 90 120 129 59 81 91 80 59 86 81 152 68 78 98 54 56 53 60 131 127 76 121 62 69 44 60 112 148 91 118 57 66 142 63 57 94 134 94 70 45 117 102 50 74 95 61 73 54 82 115 57 56 100 66 69 56 77 134 98 56 87 120 63 55 89 121 131 58 80 93 81 58 87 80 152 68 78 99 53 58 51 62 132 127 75 122 60 70 44 61 112 148 89 120 56 66 Fig.2. Embedded Image Block 141 64 57 93 134 94 69 45 116 103 50 74 95 62 72 55 82 114 56 56 100 67 68 56 77 133 98 57 87 119 63 55 89 121 130 58 80 92 80 59 86 81 152 68 78 98 53 57 52 61 131 127 76 121 61 70 44 60 112 148 90 119 57 66 Fig.3. Extracted Image Block The overall data embedding process of the proposed method is explained in fig.4. If compared with the Tian method, the location map is generated before the data embedding process whereas in Tian method, the location map is generated after the difference expansion by setting the appropriate threshold T. By setting the proper threshold, visual quality of an image can be improved or reduced. For higher threshold, the PSNR value is poor and for lower threshold, the PSNR value is good. But in the proposed method, there is no need to set the threshold value. Fig.1. Original Image Block ISBN: 978-1-61804-344-3 204

Table 1. PSNR Results of Tian Method Tian Method Image Name No. of Iterations 1 st 2 nd 3 rd Baboon 28.40 20.12 16.03 Bridge 28.87 22.44 19.40 Camera 31.30 24.77 20.36 Columbia 33.71 25.81 23.53 Couple 30.93 23.56 19.92 Crowd 32.77 27.08 22.63 Lena 33.90 25.50 20.40 Fig.4. Data Embedding Process 4 Experimental Results The experiment of the algorithm is done in MATLAB. Various grayscale images are tested, like baboon, bridge, camera, Columbia, lena, etc. For multilayer data embedding, both the methods are tested successfully on different images. Three iterations are considered for testing. Its visual is measured in terms of PSNR as shown in Table 1 and Table 2. The PSNR of image Baboon using Tian method is 16.03dB after third iteration whereas the proposed method shows the PSNR 49.26dB. For testing the Tian method, the threshold value is set to largest among the collected differences. From Table 1 and Table 2, it has been observed that with increasing number of iteration the PSNR of the image is decreases as shown in fig.5. Fig.6 shows that the embedding capacity of an image is also decreases with increasing number of iteration. After some limit of iteration, the embedding capacity of an image moves to zero. But the proposed method can embed large amount of data by applying number of iteration with retaining high quality of an image. Table 2. PSNR Results of Proposed Method Image Name Proposed Method No. of Iterations 1 st 2 nd 3 rd Baboon 52.40 50.08 49.26 Bridge 52.91 51.77 48.22 Camera 53.06 50.87 50.05 Columbia 51.98 49.90 49.14 Couple 53.61 51.16 50.31 Crowd 53.37 50.68 49.76 Lena 53.01 50.83 50.06 ISBN: 978-1-61804-344-3 205

Fig.7. No. of Iterations Vs PSNR Fig.8. No. of Iteration Vs Embedding Capacity 5 Conclusion Difference expansion is used for reversible data embedding for image authentication as well as for covert communication. For the covert communication, it is expected that the more data should be embedded into a cover median. A difference expansion technique provides this feature with multilayer data embedding. But for multilayer data embedding, precaution should be taken that the visual quality should not be degraded. In proposed method, the cover media is preprocessed before applying the difference expansion so that visual quality retain as high as possible. [4] M. U. Celik, G. Sharma, A. M. Tekalp, and E. Saber, Reversible Data Hiding, in Proc. Int. Conf. Image Processing, vol. II, Sept. 2002, pp. 157-160.. [5] G Xuan, J Zhu, J Chen, Y Q Shi, Z Ni and W Su, Distortionless Data Hiding Based on Integer Wavelet Transform, Electronic Letters, vol. 38, no. 25, pp. 1646-1648, Dec. 2002. [6] C. De Vleeschouwer, J. F. Delaigle and B. Macq, Circular Interpretation of Bijective Transformation in lossless watermarking for media asset management, IEEE Tran. Multimedia, vol. 5, pp. 97-105, Mar. 2003. [7] J Tian, Reversible Data Embedding Using a Difference Expansion, IEEE Trans on Circuit and System for Video Technology, Vol 13, No. 8 August 2003. [8] A. M. Alattar, Reversible Watermark using the Difference Expansion of a Gerneralized Integer Transform, IEEE Trans. on Image Processing, vol. 13, no. 8, pp. 1147-1156, Aug. 2004. [9] Z Ni, Y Q Shi, N Ansari, W Su, W Sun, and X Lin, Robust Lossless Image Data Hiding Designed for Semi Fragile Image Authentication,, IEEE Trans. on circuit and system video technology, vol. 18, no. 4, april 2008. [10] D. B. Satre, R. V. Pawar, Preserve Robustness for image Data Hiding, 2 nd International Conference on Computer Research and Development, pp. 707-711, may 2010. References: [1] B. Macq, Lossless Multiresolution Transform for Image Authenticating Watermarking, in Proc. EUSIPCO, Sept. 2000, pp. 533-536. [2] J. Fridrich, M. Goljan, and R. Du, Lossless Data Embedding-new Paradigm in Digital Watermarking, EURASIP J. Appl. Signal Processing, vol. 2002, no. 2. Pp. 185-196. Feb. 2002. [3] T. Kalkar and F. M. J. Willems, Capacity Bounds and Constructions for Reversible Data Hiding,, in Proc. 14 th Int. Conf. Digital Signal Processing, vol. 1, July 2002, pp. 71-76. ISBN: 978-1-61804-344-3 206