HIDING DATA IN IMAGES BY SIMPLE LSB SUBSTITUTION full report
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24012010, 05:35 PM
HIDING DATA IN IMAGES BY SIMPLE LSB SUBSTITUTION full report.DOC (Size: 430.5 KB / Downloads: 323) A PAPER PRESENTATION ON HIDING DATA IN IMAGES BY SIMPLE LSB SUBSTITUTION Hiding data in images by simple LSB substitution Abstract In this paper, a datahiding scheme by simple LSB substitution is proposed. By applying an optimal pixel adjustment process to the stego image obtained by the simple LSB substitution method, the image quality of the stegoimage can be greatly improved with low extra computational complexity. The worst case meansquareerror between the stegoimage and the coverimage is derived. Experimental results show that the stegoimage is visually indistinguishable from the original coverimage. The obtained results also show a significant improvement with respect to a previous work. Keywords: Data hiding; LSB substitution 1. Introduction Data hiding is a method of hiding secret messages into a covermedia such that an unintended observer will not be aware of the existence of the hidden messages. In this paper, 8bit grayscale images are selected as the cover media. These images are called coverimages. Coverimages with the secret messages embedded in them are called Stegoimages. For data hiding methods, the image quality refers to the quality of the stegoimages. In the literature, many techniques about data hiding have been proposed [15]. One of the common techniques is based on manipulating the least significant bit (LSB) planes by directly replacing the LSBs of the coverimage with the message bits. LSB methods typically achieve high capacity. Wang et al. [6] proposed to embed secret messages in the moderately significant bit of the coverimage. A genetic algorithm is developed to find an optimal substitution matrix for the embedding of the secret messages. They also proposed to use a local pixel adjustment process (LPAP) to improve the image quality of the stegoimage. Unfortunately, since the local pixel adjustment process only considers the last three least significant bits and the fourth bit but not on all bits, the local pixel adjustment process is obviously not optimal. The weakness of the local pixel adjustment process is pointed out in Ref. [7]. As the local pixel adjustment process modifies the LSBs, the technique cannot be applied to data hiding schemes based on simple LSB substitution. Recently, Wang et al. [8] further proposed a datahiding scheme by optimal LSB substitution and genetic algorithm. Using the proposed algorithm, the worst meansquareerror (WMSE) between the coverimage and the stegoimage is shown to be 1/ 2 of that obtained by the simple LSB substitution method. In this paper, a datahiding scheme by simple LSB substitution with an optimal pixel adjustment process (OPAP) is proposed. The basic concept of the OPAP is based on the technique proposed in Ref [7]. The operations of the OPAP is generalized. The WMSE between the coverimage and the stegoimage is derived. It is shown that the WMSE obtained by the OPAP could be less than 1/2 of that obtained by the simple LSB substitution method. Experimental results demonstrate that enhanced image quality can be obtained with low extra computational complexity. The results obtained show better performance than the optimal substitution method described in Ref. [8]. The rest of the paper is organized as follows. Section 2 briefly describes the simple LSB substitution. In Section 3, the optimal pixel adjustment process is described and the performance is analyzed. Experimental results are given in Section 4. Finally, Section 5 concludes this paper. 2. Data hiding by simple LSB substitution In this section, the general operations of data hiding by simple LSB substitution method is described. Let C be the original 8bit grayscale coverimage of pixels represented as (1) M be the nbit secret message represented as (2) Suppose that the nbit secret message M is to be embedded into the k rightmost LSBs of the coverimage C. Firstly, the secret message M is rearranged to form a conceptually kbit virtual image represented as (3) Where the mapping between the nbit secrets message M = { } and the embedded message = { } can be defined as follows: Secondly, a subset of pixels is chosen from the coverimage C in a predefined sequence. The embedding process is completed by replacing the k LSBs of by Mathematically, the pixel value of the chosen pixel for storing the kbit message is modi7ed to form the stegopixel as follows: (4) In the extraction process, given the stegoimage S, the embedded messages can be readily extracted without referring to the original coverimage. Using the same sequence as in the embedding process, the set of pixels storing the secret message bits are selected from the stegoimage. The k LSBs of the selected pixels are extracted and lined up to reconstruct the secret message bits. Mathematically, the embedded message bits can be recovered by = (5) Suppose that all the pixels in the coverimage are used for the embedding of secret message by the simple LSB substitution method. Theoretically, in the worst case, the PSNR of the obtained stegoimage can be computed by (6) Table 1 Worst PSNR for k = 15 by simple LSB substitution   k 1 2 3 4 5 PSNR 48.13 38.59 31.23 24.61 18.30   Table 1 tabulates the worst PSNR for some k = 15. It could be seen that the image quality of the stegoimage is degraded drastically when k 4. 3. Optimal pixel adjustment process: In this section, an optimal pixel adjustment process (OPAP) is proposed to enhance the image quality of the stegoimage obtained by the simple LSB substitution method. The basic concept of the OPAP is based on the technique proposed in Ref. [7]. Let be the corresponding pixel values of the ith pixel in the coverimage C, the stegoimage obtained by the simple LSB substitution method and the refined stegoimage obtained after the OPAP. Let be the embedding error between and . According to the embedding process of the simple LSB substitution method described in Section 2, is obtained by the direct replacement of the k least significant bits of with k message bits, therefore, (7) The value of can be further segmented into three intervals, such that Interval 1: Interval 2: Interval 3: (8) Based on the three intervals, the OPAP, which modifies to form the stegopixel , can be described as follows: Case 1 ( If, then otherwise ; Case 2 ; Case 3 If , then Otherwise . Let be the embedding error between and . can be calculated as follows: Case 1 and Case 2 and Case 3 Case 4 and Case 5 and From the above five cases, it can be seen that the absolute value of may fall into the range only when (Case 2) and (Case 5); while for other possible values of falls into the range . Because is obtained by the direct replacement of the k LSBs of with the message bits, and are equivalent to and , respectively. In general, for grayscale natural images, when , the number of pixels with pixel values smaller than or greater than 256  is insignificant. As a result, it could be estimated that the absolute embedding error between pixels in the coverimage and in the stegoimage obtained after the proposed OPAP is limited to (9) Let WMSE and WMSE* be the worstcase meansquare error between the stegoimage and the coverimage obtained by the simple LSB substitution method and the proposed method with OPAP, respectively. According to Eq. (9) WMSE* can be derived by WMSE* (10) Combining Eqs. (6) and (10), we have WMSE* WMSE when k =1; = (4/9)WMSE when k=2; (16/49)WMSE when k=3; (64/225)WMSE when k=4; (11) Equation (11) reveals that WMSE*<1/ 2 WMSE, for k 2; and WMSE* (1/4) WMSE when k = 4. This result also shows that the WMSE* obtained by the OPAP is better than that obtained by the optimal substitution method proposed in Ref. [8] in which WMSE* = (1/2) WMSE. Moreover, the optimal pixel adjustment process only requires a checking of the embedding error between the original coverimage and the stego image obtained by the simple LSB substitution method to form the final stegoimage. The extra computational cost is very small compared with Wangâ„¢s method [8], which requires huge computation for the genetic algorithm to find an optimal substitution matrix. 4. Experimental results This section presents experimental results obtained for two coverimage sets. The first set of coverimages consists of four standard grayscale images, 'Lena', 'Baboon', 'Jet' and 'Scene', each of 512 Ãƒâ€”512 pixels, as depicted in fig. 1. Fig 1. The first set cover images of size 512 512 pixels. The second set consists of 1000 randomly generated grayscale images. There are two set of secret messages. The first set of secret message consists of 1000 randomly generated message of 512 Ãƒâ€” 512 Ãƒâ€” k bits, where k refers to the number of LSBs in the cover image pixels that are used to hold the secret data bits. For example, suppose that the last two LSBs of the cover image pixels are used to hold the secret data, then the secret data is of size 512 Ãƒâ€” 512 Ãƒâ€” 2 = 524 288 bits. The second set consists of the reducedsized images of the grayscale image 'Tiff' as shown in fig. 2. Fig 2. Test image used as second set of secret message. The reducedsized images are of size 512 Ãƒâ€” 256 pixels (for 4bit insertion), 384 Ãƒâ€” 256 pixels (for 3bit insertion), 256 Ãƒâ€” 256 pixels (for 2bit insertion) and 256 Ãƒâ€” 128 pixels (for 1bit insertion), respectively. The results of embedding the first set of secret messages into the first set of coverimages are listed in Table 2. Referring to Table 2, the column labeled OPAP is our proposed Table 2, method with the optimal pixel adjustment process; the column labeled LSB is the simple LSB substitution method; and the column labeled OLSB in the optimal LSB substitution method proposed in Ref. [8]. For the OPAP and LSB methods, the obtained PSNR values are the average values of embedding the 1000 sets random messages into the coverimages. For the OLSB method, for k =1; 2, the obtained PSNR values are the average values of embedding the 1000 sets random messages into the cover images, for k = 3, the obtained PSNR values are the average values of embedding the 10 out of 1000 sets random messages into the coverimages while for k = 4, no experiments are conducted due to the large number of searching space for the optimal substitution matrix. The results reveal that our proposed method has much better performance than the LSB and OLSB methods for k =24. The results of embedding the reducedsized image of fig. 2 into the first set of coverimages are listed in Table 3. The results also reveal that our proposed method has much better performance than the LSB and OLSB methods for k =24. Table 4 also shows the percentage of cover image pixels associated with the five cases: Case 1 ( and Case 2 and Case 3 Case 4 and Case 5 and (12) Table 2. The results of embedding the random messages into the first set of coverimages Cover image k OPAP LSB OLSB Lena 1 51.1410 51.1410 51.1483 2 46.3699 44.1519 44.1651 3 40.7271 37.9234 37.9467 4 34.8062 31.7808  Baboon 1 51.1414 51.1414 51.1477 2 46.3691 44.1579 44.1619 3 40.7253 37.9226 37.9480 4 34.8021 31.8588  Jet1 1 51.1405 51.1405 51.1478 2 46.37000 44.1149 44.1276 3 40.7273 37.9557 37.9978 4 34.8065 31.8487  Scene1 1 51.1410 51.1410 51.1480 2 46.3702 44.1497 44.1628 3 40.7270 37.8914 37.9849 4 34.806 31.8467  Table 3 The results of embedding the reducedsized image of fig. 2 into the first set of coverimages Cover image k Case 1(%) Case 2(%) Case 3(%) Case 4(%) Case 5 Lena 2 9.52 0 86.55 3.93 0 3 14.15 0 80.86 4.99 0 4 21.30 0 73.27 5.43 0 Baboon 2 9.53 0.01 86.51 3.95 0 3 14.03 0.02 80.90 5.05 0 4 20.78 0.05 73.85 5.32 0 Jet 2 9.67 0 86.32 4.01 0 3 13.91 0 81.20 4.89 0 4 20.31 0 74.22 5.47 0 Scene 2 9.58 0 86.53 3.89 0 3 14.17 0.01 80.78 5.04 0 4 21.01 0.01 73.74 5.24 0 Table 4 The percentage of cover image pixels associated with the five cases (Eq.12) when the reducedsized images of Fig.2 are embedded into the cover images. Cover image k Case 1(%) Case 2(%) Case 3(%) Case 4(%) Case 5 Lena 2 9.52 0 86.55 3.93 0 3 14.15 0 80.86 4.99 0 4 21.30 0 73.27 5.43 0 Baboon 2 9.53 0.01 86.51 3.95 0 3 14.03 0.02 80.90 5.05 0 4 20.78 0.05 73.85 5.32 0 Jet 2 9.67 0 86.32 4.01 0 3 13.91 0 81.20 4.89 0 4 20.31 0 74.22 5.47 0 Scene 2 9.58 0 86.53 3.89 0 3 14.17 0.01 80.78 5.04 0 4 21.01 0.01 73.74 5.24 0 For illustrative purpose, fig. 3 shows a pair of stegoimages obtained by embedding the reducedsized image 'Tiff' of size 512 Ãƒâ€” 256 pixels into the coverimage 'Lena' of size 512 Ãƒâ€” 512 pixels using the simple LSB method and the proposed OPAP method. From fig. 3(a) (stegoimage obtained by the simple LSBsubstitution method), one can see some false contours appearing on the shoulder of 'Lena'. The unwanted artifacts may arise suspicion and defeat the purpose of steganography. However, there is no such artifacts appearing on the stegoimage (fig. 3(b)) obtained by the proposed method. The visual quality of stegoimages obtained by the proposed method is much better than that of obtained by the simple LSBsubstitution method. To further evaluate the performance of the proposed method, the reducedsized image of fig. 2 is embedded into 1000 sets randomly generated coverimages and the obtained average PSNR values are listed in Table 5. (a) (b) Fig. 3. Stegoimages obtained by (a) Simple LSBsubstitution method; (b) Proposed method, where the secretimage is of size 512 Ãƒâ€” 256 pixels (4bit insertion). Table 5 The results of embedding the reducedsized image of fig. 2 into the second set of coverimages.   Cover image k OPAP LSB   Random 1 51.1410 51.1410 2 46.3215 44.0217 3 40.6023 37.8621 4 34.4868 31.337   The results show that similar PSNR values can be obtained for different type of coverimages. 5. Conclusion: In this paper, a data hiding method by simple LSB substitution with an optimal pixel adjustment process is proposed. The image quality of the stegoimage can be greatly improved with low extra computational complexity. Extensive experiments show the effectiveness of the proposed method. The results obtained also show significant improvement than the method proposed in Ref. [8] with respect to image quality and computational efficiency. Use Search at http://topicideas.net/search.php wisely To Get Information About Project Topic and Seminar ideas with report/source code along pdf and ppt presenaion



