Digital Signal Processing

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1 Digital Signal Processing 20 (2010) Contents lists available at ScienceDirect Digital Signal Processing wwwelseviercom/locate/dsp High capacity lossless data embedding technique for palette images based on histogram analysis Noura A Saleh a, Hoda N Boghdady a,, Samir I Shaheen b, Ahmed M Darwish b a National Telecommunication Instiute, Cairo, Egypt b Computer Eng Dept, Faculty of Engineering, Cairo University, Egypt article info abstract Article history: Available online 18 February 2010 Keywords: Histogram Palette images Lossless data hiding Steganography Stego image Recently data embedding over images has drawn tremendous interest, using either lossy or lossless techniques Although lossy techniques can allow large hiding capacity, host image cannot be recovered with high fidelity Some applications require exact recovery of the host image, ie in medicine patient data can be embedded without affecting the medical image In general lossless data hiding techniques suffer from limited capacity as the host image should be kept intact In this paper a lossless embedding technique is proposed In this technique image histograms are analyzed to identify the embedding capacity of different image types Histogram maxima and minima are used in embedding capacity estimation The proposed technique gives hiding capacity that can reach up to 50% of the host image size for images with large homochromatic regions (cartoons-like) In fact, our study showed that the embedding capacity is not only affected by the host image size but also by its histogram distribution The data embedding and extraction is performed using simple processing operations that can save on power consumption for wireless devices 2010 Elsevier Inc All rights reserved 1 Introduction Steganography is one such type that embeds data in a cover or a host image creating a stego image Imperceptibility, robustness, data embedding capacity, hidden data security and computational complexity, etc, are key issues for good data hiding techniques in digital images Hiding techniques can be used for either secure or non-secure applications For secure application the host image is not of concerned, therefore capacity can be increased while increasing image distortion On the other hand increasing image distortion can attract hacker attention For non-secure application, like embedding personal data in medical reports, it is necessary to retrieve the host image without error or distortion It can be seen that almost all applications require lossless techniques to insure host image quality accompanied with large hiding capacity One of the most popular cover images are the palette based images, they are popular in multimedia and internet applications Each palette image is composed of a color palette and a set of color indices Since the indices of color palette images are not color values themselves, this make them challenging for data hiding One of the most common data embedding techniques is the Least Significant Bits (LSB) [1] It simply embeds data by replacing the LSBs of the image pixels However how to decide on the maximum embedding capacity for each pixel is still an open issue Entropy based technique was used for detecting the suitable areas in the image where data can be embedded with minimum distortion [2,3] Other techniques hide data by extracting the features of the host image by means of Block * Corresponding author addresses: noura_nti@yahoocom (NA Saleh), boghdady@yahoocom (HN Boghdady), Sshaheen@ieeeorg (SI Shaheen), Darwish@ieeeorg (AM Darwish) /$ see front matter 2010 Elsevier Inc All rights reserved doi:101016/jdsp

2 1630 NA Saleh et al / Digital Signal Processing 20 (2010) Table 1 Colors distribution in the image Type Image name Highest peak in % Num of zeroes Number of colors occupying percent of image pixels 5% 10% 20% 30% 50% 70% 90% 1 Car Pink card Caricature Graph Fractal Butterfly Fractal Beach Pictures Deer Swan Library Truncation Coding (BTC) algorithm and combine it with the message to be hidden through XOR operation [4] The main draw back of this scheme is that it requires possessing both the host image and the stego image to recover the hidden message Another approach was proposed in [5] based on adaptive data hiding in palette images by color ordering and mapping The main idea of this scheme is based on filtering the indices unsuitable for changes, then embedding the message into the rest of indices The color of a data embeddable pixel is modified to be an optimal one by selecting the least distorting color around the pixel from the palette The method raised the stego image quality but reduced the hiding capacity Pan et al [6] proposed a data hiding technique for palette images with a few color existences as in cartoon images that are often synthetic graphics without complicated color and texture variations making invisible data embedding difficult The scheme embeds the message in those pixels close to the boundaries Although, experimental results showed a good visual quality it has a very low hiding capacity Fridrich et al [7] and Hongmei et al [8] proposed a lossless data embedding technique for palette images, based on copying the most frequent color in the image several times in the color palette The main draw back of these algorithms is the limited hidden capacity; about 10% of the host image size was reported In this paper we propose a lossless data embedding technique for 256-color palletized images with capacity optimization This technique is useful for applications that require the host image at the receiver with high fidelity The images are classified according to their histogram distributions, 3 types of images are defined In fact, this technique gives good results for images with large homochromatic regions that, previously, presented a problem for data hiding [6], like cartoon images (defined as type-1 image in this paper) First, image classification is explained in the next section, and then a full description of the proposed algorithm is presented in Section 3 Capacity calculation and optimization are given in Section 4, in Section 5 the embedding and extracting processes are explained Simulation results are discussed in Section 6, last a conclusion is presented in Section 7 2 Image classification The embedding technique is based on the number of the most and least used colors in the image Therefore a complete analysis of the image histogram is important to identify each color repetition in the image In this paper images are classified according to their histogram distribution, color repetition is calculated as a percentage of the number of pixels For each image the peak color percent is calculated (most used color in the image), then the number of colors representing 30, 50 and 70% of the total number of pixels are also identified Table 1 gives the highest histogram peak, number of zeroes (unused color), and number of colors occupying the image pixels while the later is given in percentage (5, 10, 20, 30, 50, 70 and 90%); ie how many colors occupy 20% or 50% and so on It can be seen from Table 1 that 50% of the image uses 1 or 3 colors in type-1 images while in type-2 50% of the image is occupied by 10 s of colors (40 50 colors) In type-3 10 colors exist in 50% of the image Although type-2 images appear to have many peaks in their histogram, their values don t exceed 4% which means colors are, almost, evenly distributed over the image Figs 1 3 show histograms of the different image types respectively (some of the images corresponding to these histograms are shown in Fig 6) 3 The proposed algorithm The proposed embedding algorithm uses the indices corresponding to zero values in the histogram (ie indices pointing to unused colors) to repeat the most used color(s) After repeating colors, the indices of the repeated colors are used to map data bits according to a predefined mapping scheme Fig 4 shows a sorted histogram for the image pinkcard where the indices have zero values, ie they point to the unused colors in the palette Therefore, the most used color belongs to index 0 and the zeroes locations belong to

3 NA Saleh et al / Digital Signal Processing 20 (2010) Fig 1 Type-1 images histogram percentage; (a) car, (b) pink card, (c) caricature, (d) graph Fig 2 Type-2 images histogram percentage; (a) fractal 4, (b) butterfly, (c) fractal 1, (d) beach

4 1632 NA Saleh et al / Digital Signal Processing 20 (2010) Fig 3 Type-3 images histogram percentage; (a) pictures, (b) deer, (c) swan, (d) library Fig 4 Sorted histogram Fig 5 The mapping matrix indices A mapping matrix is created by repeating the color indexed 0 15 times (2 4 1) in the indices containing zeroes in the histogram 4-bits data mapping is now possible following the mapping scheme shown in Fig 5 Note that the color indexed 0 is the most used one and occupies 263% of the image In this case the embedding capacity is the number of pixels belonging to the color 0 multiplied by the number of bits used in the mapping procedure (4 bits in this case) The embedding capacity can be increased by using more indices of the unused colors by repeating another peak color; the color indexed 1 can be repeated 7 times in indices allowing mapping 3 data bits more The new capacity is now increased by the number of pixels belonging to the color indexed 1 multiplied by 3, and so on until all unused color indices are filled Therefore, it can be seen that the embedding capacity is not only a function of the histogram peak values but is also a function of the number of zeroes in the histogram The maximum possible number of mapping bits n 0 can be calculated

5 NA Saleh et al / Digital Signal Processing 20 (2010) Table 2 Mapping schemes for the histogram in Fig 1(b) C 1 C 2 C 3 C 4 C 5 C 6 C 7 Capacity in bits Capacity (%) using Eq (1) by simply taking the highest integer value n 0 satisfying (2 n 0 1) U where U is the number of unused colors in the palette (U = number of zeroes in the histogram) ( ) ( ) log(u + 1) log(u + 1) n 0 = Integer = Integer (1) log Mapping scheme The peak colors and the unused color indices used represent a mapping scheme In fact, an image can have many mapping schemes given by the general equation (2) U = C 1 ( 2 1 ) 1 + C 2 ( 2 2 ) 1 + +C n0 ( 2 n 0 ) n 0 ( 1 = C i 2 i ) 1 (2) Eq (2) insures the use of all unused colors in the mapping scheme; C i can be any integer number including zero and i represents the number of bit mapped by the color C i and it varies from 1 up to n 0 For example the histogram in Fig 1(a) contains 23 zeroes, some possible mapping schemes are: 23 = 1 ( 2 1 ) ( 2 2 ) ( 2 3 ) ( 2 4 ) 1 C 1 = 1, C 2 = 0, C 3 = 1, C 4 = 1 23 = 2 ( ) + 0 ( ) + 3 ( ) + 0 ( ) C 1 = 2, C 2 = 0, C 3 = 3, C 4 = 0 The first scheme means that 1 color maps 4 bits, 1 color maps 3 bits and 1 color maps 1 bit, on the other hand the second scheme means that 3 colors are used to map 3 bits each and two colors are used to map 1 bit each The schemes can simply bewritteninanarrayformc =[C 1 C 2 C n0 ], and it reads C i = number of colors used to map i-bit, the first scheme can be written C =[1011] and the second scheme C =[2030] A software routine is used to obtain all possible mapping schemes for each image then the scheme giving the highest capacity is selected for data embedding 41 Optimum capacity The embedding capacity is calculated by summing the product of peak value by its mapping bits For example for the scheme given by C =[1011], 3 colors representing the highest 3 peaks in the histogram are used to map 1, 3 and 4 bits respectively, so the capacity = 1st peak 4 + 2nd peak 3 + 3rd peak 1iecapacity = H[0] 4 + H[1] 3 + H[2] 1, where H[i] is the histogram value of color indexed i in the sorted histogram (Fig 4) The total number of peak colors used in the mapping N peak is the sum of C i in that scheme The general formula for the embedding capacity is given by the equation: capacity = N peak 1 i=0 H[i] n i where H[i] is the histogram value for the color indexed i and n i is its corresponding mapping bit Comparing the capacity calculated in Table 2 with the one obtained if using the method in [8]: the capacity would be H[0] 1orH[0] 2orH[0] 4 as the author uses only one color with what he called 3 operation modes: the color is repeated once, twice or four times It is seen that our technique allows larger embedding capacities i=1 (3)

6 1634 NA Saleh et al / Digital Signal Processing 20 (2010) Table 3 Mapping schemes for the histogram in Fig 3(a) C 1 C 2 C 3 C 4 C 5 C 6 C 7 Capacity in bits Capacity (%) Table 4 Mapping schemes for the histogram in Fig 3(b) C 1 C 2 C 3 C 4 C 5 C 6 C 7 Capacity in bits Capacity (%) An algorithm is used to generate all possible C-arrays then the capacity is calculated for all mapping schemes then the scheme corresponding to the maximum capacity is selected Examples of mapping schemes for different images are given in Tables 2, 3 and 4 for the histograms shown in Figs 1(b), 3(a) and 3(b) respectively It can be seen from Table 2 that the array C =[01011] (row 139, scheme containing maximum mapping bit n 0, n 0 = 5 in this case) gives a capacity of 2866% while the array C =[21220] (row 122) gives a maximum capacity of 3539% This prove that using 7 peaks to map 4, 3, 2 and 1 bits is better than using 3 peaks and map n 0 bits In type-3 images where more peaks are available, the maximum capacity is realized with large number of peaks as seen from Tables 3 and 4 5 The embedding and extracting processes A C-array is selected for data embedding, and knowledge of that C-array at the receiver is essential to extract the data back Which C-array is selected and how it is transferred to the receiver can be part of the data embedding security issues to be studied In this work, the C-array corresponding to maximum capacity is the one selected for the embedding process 51 The embedding process Once the mapping scheme (C-array) is decided, the mappings matrix can be generated as explained in the previous subsection (Fig 5) The embedding process starts by scanning the host image for the indices of the used N peak colors For each color i n i bits are read from the data stream The pixel of that peak color will now points to a new index according to the mapping matrix (Fig 5) Note that the color of that pixel remains unchanged only its index changes For example, to embed the following stream of data: , using the mapping scheme in Fig 5, the image will be scanned for color indexed 0 Every time the color 0 is found 4 bits are read from the data (1100), then that pixel will point to index 244 according to the mapping matrix shown in Fig 5; next pixel pointing to 0 (0101) are read then pixel index will point to 237 and so on until all data are embedded Same procedure is followed for scheme with many peak colors, the image is scanned and each time a peak color is found corresponding data bits are read and pixel indices are changed according to the mapping matrix 52 The extraction process To extract the information back at the receiver side, an inverse process is repeated The stego image is scanned for all indices in the mapping scheme Each index is then converted into its mapping bits and data is retrieved The C-array is required at the receiver to regenerate the mapping matrix It can be seen that only the stego image and the C-array are required at the receiver

7 NA Saleh et al / Digital Signal Processing 20 (2010) Fig 6 Images corresponding to the analyzed histograms 6 Simulation results The algorithm was tested using images with different histograms as mentioned earlier The presented technique showed very good embedding capacity especially for type-1 images as given in Table 4 This was expected as the few peaks of the histogram represent very high percentage of the total number of pixels The results obtained showed a capacity increase as the number of unused colors increases for both type-1 and type-3 images Although, for very large number of unused colors type-3 gives better results as more peaks can be used to map more bits while type-1 images contains 3 or 4 peaks only For type-2 images our technique gave low embedding capacity as in fact no peaks or zeroes can be identified From the above discussion we realize that the shape of the image histogram plays an important role in estimating the embedding capacity of each image For example, the histogram shown in Fig 1(b) has 3 peaks representing more than 50% of the total number of pixels The selected C-array for this histogram (Table 5) is [ ], allowing more than 70% of the image pixels for data embedding (Table 1) The calculated capacities given in Table 5 are percentages of the images sizes calculated in bits not in pixels

8 1636 NA Saleh et al / Digital Signal Processing 20 (2010) Table 5 Mapping scheme and the capacity percentage Image Type Image size W H Unused Mapping scheme Cap (%) C 1 C 2 C 3 C 4 C 5 C 6 C 7 Car Pink card Caricature Graph Fr Butterfly Fr Beach Pictures Deer Swan Library It can be conclude that the number of zeroes is a necessary parameter for increasing embedding capacity and it should be larger than the number of peaks This explains why type-2 images have no potential for data hiding using this algorithm 7 Conclusion In this study a lossless data embedding technique for 256-color palletized images has been proposed The embedding capacity is based on the image histogram and the number of unused colors The stego image quality is not affected as the color values are the same, only used indices are changed Histogram analysis is performed in order to understand the capacity potential of different image types The unused colors in the palette have been used to optimize the embedding capacity Capacity more than 30 and 50% of the host image size has been obtained for type-3 and type-1 images respectively The extraction of the embedded data does not require the original host image at the decoder side, only the stego image is required It should be noted that the C-array can be extracted from the palette of the stego image The proposed technique can be applied for non-secure applications like embedding personal data in medical images or for copyright applications References [1] Nan-I Wu, Min-Shiang Hwang, Data hiding: Current status and key issues, International Journal of Network Security 4 (1) (January 2007) 1 9 [2] M Mohiy, et al, Data hiding technique using entropy calculation and image compression using block truncation coding, Transactions on Engineering, Computing and Technology 3 (ISSN ) (December 2004) [3] Swetha Kurup, G Sridhar, V Sridhar, Entropy based data hiding for document images, Transactions on Engineering, Computing and Technology 5 (ISSN ) (April 2005) [4] Shu-Fen Tu, Ching-Sheng Hsu, A BTC-based watermarking scheme for digital images, Information and Security An International Journal 15 (2) (2004) [5] Chic-Hsuan Tzeng, Zhi-Fang Yang, Wen-Hsiang Tsai, Adaptive data hiding in palette images by color ordering and mapping with security protection, IEEE Transaction on Communication 52 (5) (May 2004) [6] Gang Pan, Zhaohui Wu, Yunhe Pan, A data hiding method for few-color images, in: IEEE Proceedings of the 2002 International Conference on Acoustic, Speech, and Signal Processing, ICASSP 02, vol 4, May 2002 [7] Jessica Fridrich, Miroslav Goljan, Rui Du, Lossless data embedding for all image formats, The International Society for Optical Engineering, SPIE, 2003 [8] Hongmei Liu, Zhefeng Zhang, Jiwu Huang, Xialing Huang, Yun Q Shi, A high capacity distortion-free data hiding algorithm for palette image, in: IEEE Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS 03, vol 2, May 2003 Noura A Saleh received her BSc degree in Electronics and Communication Engineering from Helwan University, Egypt in 1992, the MSc degree in Electronics and Communication Engineering from Cairo University, Egypt in 1999, and the PhD degree in Computer Engineering from Cairo University, Egypt in 2008 Currently she is an assistant professor at the National Telecommunication Institute, Egypt Current research interests: data hiding, image processing, image and video compression, digital communications with application to image coding and transmission Hoda N Boghdady graduated from the Electronics and Communication Engineering Department, Cairo University, Egypt in 1983 She obtained MSc from Cairo University, Egypt and PhD from North Carolina State University, USA, in 1987 and 1993 respectively Currently, Deputy Head of the Transmission Department, National Telecommunication Institute, Egypt Current research interests: image transmission, data hiding, OFDM and wireless transmission system

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