Image hiding with an improved genetic algorithm and an optimal pixel adjustment process

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Eighth International Conference on Intelligent Systes Design and Applications Iage hiding with an iproved genetic algorith and an optial pixel adjustent process Lin-Yu Tseng Yung-Kuan Chan Yu-An Ho

1 Eighth International Conference on Intelligent Systes Design and Applications Iage hiding with an iproved genetic algorith and an optial pixel adjustent process Lin-Yu Tseng Yung-Kuan Chan Yu-An Ho Yen-Ping Chu Departent of Coputer Departent of Manageent Departent of Coputer Departent of Coputer Science and Engineering Inforation Systes Science and Engineering Science and Engineering Chung Hsing University Chung Hsing University Chung Hsing University Chung Hsing University Taichung, Taiwan, R.O.C. Taichung, Taiwan, R.O.C. Taichung, Taiwan, R.O.C. Taichung, Taiwan, R.O.C. Abstract Iage hiding techniques ebed a secret iage into a cover iage. The fusion of the two, called a stegoiage, fools grabbers who would not be conscious of the differences between the cover iage and. the stegoiage. A secret iage can be transferred safely using this technique. In general, we would disarrange each pixel in the secret iage and adjust all of the to for a suitable string of bits that could be ebedded. Then these bits are ebedded into the cover iage in corresponding places, and this iage would becoe a stego-iage that hides secret data. This paper suggests a new iage disarranging technique. It uses an iproved genetic algorith and an Optial Pixel Adjustent Process, OPAP, to enhance the quality of a Stego-iage. Experiental results show that a stegoiage is indistinguishable fro the cover-iage. The stego-iage can ebed bits per pixel, and the eansquare error of a stego-iage is uch lower than results for previous ethods [,,, ].. Introduction Using the Internet, secret data can be transitted speedily. However receivers and senders use various encryption ethods to prevent exposure to hacers [6-7, 9, 3, ]. In order to protect transitted data, iportant data is encrypted using encrypting and decrypting calculations. The process ay use a series of ciphers that see to be disordered nonsense to encrypt data before sending it []. At the receiving end, a decryption calculation is used. Through this ethod, encrypted data can be transitted over the Internet. Even if hacers do obtain the encoding ciphers, if they cannot obtain the original secret data, they will never uncover the inforation that was hidden in the cipher. The above-entioned ethod has one weaness: encrypted ciphers attract hacers attention. Therefore, if ciphers can be ebedded into a general, natural iage, hacers would be isdirected. This ethod is called data hiding. Iage hiding techniques ainly deal with a secret iage ebedded within a cover iage to get a Stego-iage. This Stego-iage conceals hidden data without advertising that it is hiding anything. One coon ethod is called Leastsignificant-bit (LSB) [5, 8, 5-6]. LSB replaces a cover-iage directly, after hiding a secret iage within it. In general, bit-apped iages are coonly used. Every iage is coprised of pixels, and each pixel indicates one color. If an iage is shown in gray-scale, with a range of values fro 0 to 55, lower values signify darness, and higher values, lightness. Therefore, a gray-level iage can be adjusted by adjusting the values. At least 8 bits are required to represent these values, and the binary syste stores the fro the ost signification bit to the least signification bit a a...a. LSB substitution replaces 8 7 the least signification bit a to ae iperceptible changes that can t be recognized by huan vision. For exaple, one pixel s gray-level ay be 00. When it hides a, we odify the least signification bit to becoe pixel 0. This difference can t be recognized by huan vision. In this way, we can hide another iage. Though LSB can easily ebed secret data into an iage, it would degrade iage quality in a way that ight alert hacers. Wang et al. [0] proposed a ethod called the oderately significant bit (MSB) which ebedded secret data into a cover-iage. Here the MSB eans that the fourth bit of a gray pixel is a. When ebedding, a genetic algorith can be used to find the optial substitution atrix for each pixel, and then perfor a local pixel adjustent process (LPAP) to iprove the quality of the stego-iage. Chan et al /08 $ IEEE DOI 0.09/ISDA

2 [3] pointed out that the LPAP adjusts the fourth bit against LSB. Thus, the ethod cannot be applied for siple LSB substitution. This ethod uses a loo-up table to adjust the iage ebedding. In addition to the fourth bit of LSB, other bits ight be odified to achieve the best result. Afterwards, Wang et al. [] proposed a ethod which uses bijective apping function to disarrange the secret data prior to ebedding the into the cover iage. Because of the nuerous cobinations after disarranging, this ethod goes a step further by utilizing a genetic algorith to see and find the optial solution. Though this ethod has reduced calculating tie, acquired bijective apping function only approxiates optial solution. Chang et al. [] utilized a dynaic prograing strategy to deterine the optial solution, and even reduced the calculating tie. In 003, Thien et al., [] proposed a highhiding-capacity ethod which can ebed data via a digit by digit syste in real tie and achieve a uch higher vision quality than LSB can, while avoiding resulting artificial edges of LSB. Against iage hiding technique, Chan et al. [] proposed an optial pixel adjustent process, OPAP. The concept follows the ethod subitted by Wang et al. [0]. It handles the last bits of the iage and eploys an optial pixel adjustent process to lessen the related coplicated calculating. Although the ethod that was subitted by Wang et al. [0] has iproved the iage quality substantially, in order to provide uch greater cover iage quality, this paper proposes a whole new iage disarrange technique. We utilize an iproved genetic algorith and Optial Pixel Adjustent Process, OPAP to enhance the quality of the cover iage. The reainder of the paper is organized as follows. The adjustent procedure of the optial pixel will be introduced in the second section. The third section will introduce the hiding iage technique that is proposed in this paper. The fourth section is the experiental results of the proposed ethod, and a contrast with other ethods. The fifth section is the conclusion.. Optial pixel adjustent process This section will introduce OPAP as proposed by Chan et al. []. The ain construct of OPAP follows the ethod that was subitted by Wang et al. [0]. It does optial adjustent with the last bits of an iage, and uses low coplicated calculating. The ain calculation of OPAP is eployed as algorith opap: Algorith opap Inputs: Transfored secret iage Output: Stego-iage { C S, Cover iage C For j 0 to M- Do for i 0 to N- Do D[ od S[ If ( D [ > ) Then if ( C [ < 55 ) Then C [ + Else C [ Else if ( D [ < ) Then if ( C [ > ) Then C [ Else C [ Else C [ } Inputs are secret iage S and cover iage C; then secret iage S is rearranged to becoe S which is the sae size as the cover iage. The bit plane of S is saller than the bit plane of C which is an ebedded bit plane. M and N are the height and the width, and j and i are the coordinate of the iage. D [ is the value that subtracts secret iage S fro the place of LSB of C. If C [ = 00, S [ = 5, =, then D [ = 00 od 5 = is saller than 3. As a result, we tae 00-(-)-6=95. This way, the difference will be saller than 00+= which is the direct ebedding of LSB bits fro 00, and it could also reduce the value of the ean square error. In the end, it outputs adjusted stego-iage C. 3. The proposed ethod This section introduces a secret iage transfor ethod. This ethod utilizes a genetic algorith to deterine the optial transfor seed to get the best quality cover iage. 3.. A secret iage transfor ethod First, our ethod adjusts secret iage S to be the sae size as the cover-iage. Suppose that the value of the pixel of gray-level is g-bit pixel value, the size of the cover-iage is M N, and the size of the c c secret iage M N ; according to equation (), it can s s then obtain the inforation on the lowest nuber of deand bits of one pixel of the cover-iage in order to ebed it totally within the secret iage. Therefore, it can transfor the size of the secret iage to equal 3

3 that of the cover iage, and be shown as S. Furtherore, each pixel is shown at least as bits. g M M s s =. () M N c c Then, tae the acquired S to perfor the transfor using a rando seed. While transforing, at first it will result in a fixed size of transfor bloc expressed R = r[ 0 i, j n, where by: { } r [ { 0,,,..., } bloc size,. Moreover, according to the S will be segented by n n size, S = q[ ] 0 i i, j n, here becoing { } q [ { 0,,,..., }. is the serial nuber of the segented blocs. Then add each S to R, and tae the reainder of. As a result, it can get a disarranged secret iage S which is shown as equation (): " S = ( S + R)od. () Assue that n is, is, and is (ean of the first bloc). There is a siple exaple as follows: S =, R =, " S = ( S + R) od = The acquired " S is the reainder of which is the result of adding S to R. Finally, ebed S " to C to get the stego-iage C. We can use equation (3) pea signal-to-noise (PSNR) and equation () ean square error (MSE) to estiate the quality difference between C and C. The definition of the function of PSNR is as follows: PSNR = 0log (55) db MSE (3) And the definition of MSE is as follow: MSE = M N () It can return to the M N ( C C ) j= i= S only by subtracting R fro " S. If there was a negative nuber in the resulting blocs, then add to return to S. 3.. Iproved genetic algorith According to all possible results of R in Section 3., n n they can bring ( ) inds of changes. Therefore, calculating every change would tae lots of tie. The following introduce how to find out the approxiate optial solution by algorith A. Algorith A Inputs: Define the fitness function, nuber of generation size of population, crossover rate, utation rate and selection rate. Output: The best chroosoe. { enerate the chroosoes of the first generation by rando selection. For g 0 to generation Do Optial Pixel Adjustent Process by chroosoe Evaluate the fitness value MSE by chroosoe If Satisfy the ending condition Than Obtain the best solution Brea Select the better chroosoes Recobine new chroosoes by using crossover Recobine new chroosoes by using utation. } First, it has to input the secret iage, transfored by the chroosoes that were built in the first generation, and then ebed it into the cover iage as well as calculate the value of MSE. In the second step, it has to execute a loop according the set nubers of generation. If it is satisfied with the set value of MSE while executing, it will then brea fro the loop and output the suitable chroosoe. If it is not satisfied with the value of MSE, it will choose a better one fro aong these chroosoes. Furtherore, it will result a new generation of chroosoes through crossover and utation, and then transfer the to the next generation for evaluation. When it is satisfied with the nuber of ties of generation or the set value of MSE, it will stop reproducing and output the best transfored bloc. The procedure of crossover can be handled by one point or two points. At first, transfor the two diension R transfor into a one diension bloc. The handling process is as follows: a a a a 0 3 a a5 a 6 a 7 [ a a... a ]. 0 5 a a a a a a a a 3 5 Then, tae this one diension bloc as a chroosoe. If there were two chroosoes, and which are shown as Figure (a), the gene of is 3

4 shown by boldface while the gene of is in regular font. After a one point crossover, the result is shown as Figure (b). It results in new chroosoes and (a) (b) Figure. A siple exaple: after crossover, (a) and (b) becoe and. Mutation ainly provides each gene with a probability. If the gene falls into the range of probability, then it will reproduce a new eleent at rando. Figure is the utation results of. Boldface nubers are the reproduced genes which fall into a given probability. " Figure. The result is utation of. Boldface nubers are the reproduced genes which fall into a given probability Discussion of set way of atrix There are various inds of design ethods of transfor bloc. One anner uses a n n bloc R to handle a whole iage. The second anner segents the iage into four sub-iages and individually trains each sub-iage by a ( n / ) ( n / ) bloc. Then, it will get the best four ( n / ) ( n / ) blocs, with the total size equal to a n n bloc. The third anner segents the iage into 6 sub-iages, and individually trains each sub-iage by a ( n / ) ( n / ) bloc. Then, it will get the best 6 ( n / ) ( n / ) blocs. Here the total size is equal to a n n bloc. The experiental result will show that the third anner spends uch less tie while obtaining the highest quality.. Experiental results This section ainly discusses the quality of a hidden iage by the ethod that is proposed in this paper, by experient. The progra is written in C++ Builder, and executed under an AMD Athlon 600+ and RAM WinXP syste. The size of the experiental stego-iage is 5 5 pixels graylevel, as shown as Figure 3(a) and 3(b). The secret iage is 56 5 pixels gray-level iages, shown as Figure 3(b), (c) and (d). Therefore, the data hiding rate is bits of each pixel. Then, an 8 8 bloc R, four bloc R, and bloc R are taen as the transfor bloc, and the secret iage is disarranged by three ethods: a whole iage, sub-iages, and 6 sub-iages. As a result, these 56 bits are secret eys. First, we transfor a secret iage by one 8 8 bloc R. In the process of genetic algorith, it will produce forty R, and treat the lie chroosoes. If these forty R were handled by set 0.5 crossover rates and 0.3 utation rates, it would reproduce another new forty chroosoes. Then, we pic out 0 chroosoes whose value of MSE are uch lower aong the 80 chroosoes, and tae these 0 chroosoes as the next generation chroosoes. Therefore, the selection rate is 0.5. The last generation, which is tested 000 ties, will tae the lowest value of MSE which is produced by R as transfor bloc. After 000 generations, we will now that the MSE still can be iproved when it is transfored at the 75st generation. Therefore, the 000 cycles of generation tae 33 inutes and 9 seconds, and the value of MSE is.3. Then, the stego-iage is segented into four subiages and each sub-iage is transfored individually by a bloc. In the process of genetic algorith, it will produce forty R for each sub-iage, tae the as chroosoes and handled by set 0.5 crossover rates, 0.3 utation rates and 0.5 selection rates. After 500 generations, it will tae the lowest value of MSE which is produced by a bloc as transfor bloc. There will be four blocs in the whole iage, and the cobined size will equal that of a 8 8 bloc. After 00 generations, we will now that the iproved range of generation deceases. It is finished after 500 ties generation, taes 7 inutes and 3 seconds, and the value of MSE is Finally, the stego-iage is segented into sixteen sub-iages, and each sub-iage is individually transfored by one bloc. In the process of genetic algorith, it will be handled by set 0.5 crossover rates, 0.3 utation rates and 0.5 selection rates. After 00 generations, it will tae the lowest value of MSE which is produced by the bloc as 33

5 the transfor bloc. There will be sixteen blocs in the whole iage, and the cobined size will be equal to that of the 8 8 bloc. After 0 generations, we will now that the iproved range of generation greatly decreases. It is finished after 00 ties generation, taes four inutes and five seconds, and the value of MSE is By genetic algorith, it can set the initial transfor bloc at rando fro the enorous transfor bloc, then use crossover, utation and selection to produce new generation, and explore if there is a better transfor bloc. The experient too lots of test tie, but in reality, it can set fewer nubers of generations to deterine the better transfor bloc. Table shows the results of the proposed ethods: 8 8, four, and sixteen of the cover iage Lena and three secret iages. It also lists the contrast result of siple LSB substitution, genetic algorith [], dynaic prograing [] and optial pixel adjustent process [, ]. Table shows the contrast between the proposed ethod and other ethods of the cover iage pepper and other three secret iages. We understand that the proposed ethod can obtain the least value of MSE. 5. Conclusions While transferring data over the Internet, we face any unnowns. To transfer data securely, a safety syste is needed. This paper proposes an effective, siple iage-hiding technique to iprove the quality of stego-iages. It can raise the quality of the stegoiage, and hacers ay ignore the stego-iage with its ebedded secret iage. The experiental result shows that the stego-iage and cover iage can not be distinguished. The hiding capacity can reach four bits in a pixel, and the eansquare error of the stego-iage is uch lower than for the iages which were produced by previous ethods. In the future, we hope that we can find an even better technique to hide ore data in a cover iage. Furtherore, we also have to ae efforts in cover iage odification. Less odifying or even no odifying should be the focus of future research. based on odulus function, Pattern Recognition, vol. 36, no., 003, pp [3] C. K. Chan and L.M. Cheng, Iproved hiding data in iages by optial oderately significant-bit replaceent, IEE Electronics Letters, vol. 37, no. 6, 00, pp [] C. K. Chan and L. M. Cheng, Hiding data in iage by siple LSB substitution, Pattern Recognition, vol. 37, no. 3, 00, pp [5] E. Adelson, Digital signal encoding and decoding apparatus, US Patent, no , 990. [6] J. Wang and L. Ji, A region and data hiding based error concealent schee for iages, IEEE Transactions on Consuer Electronics, vol. 7, no., 00, pp [7] K. L. Chung, C. H. Shen, and L. C. Chang, A novel SVD- and VQ-based iage hiding schee, Pattern Recognition Letters, vol., no. 9, 00, pp [8] L. F. Turner, Digital data security syste, Patent IPN WO 89/0895, 989. [9] L. M. Marvel, C.. Boncelet, and C. T. Retter, Spread spectru iage steganography, IEEE Transactions on Iage Processing, vol. 8, no. 8, 999, pp [0] R. Z. Wang, C. F. Lin, and J. C. Lin, Hiding data in iages by optial oderately significant-bit replaceent, IEE Electronics Letters, vol. 36, no. 5, 000, pp []N.. Bourbais and C. Alexopoulos, Picture data encryption using SCAN patterns, Pattern Recognition, vol. 5, no. 6, 99, pp [] R. Z. Wang, C. F. Lin, and J. C. Lin, Iage hiding by optial LSB substitution and genetic algorith, Pattern Recognition, vol. 3, no. 3, 00, pp [3] T. J. Chuang and J. C. Lin, New approach to iage encryption, Journal of Electronic Iaging, vol. 7, no., April 998, pp [] T. S. Chen, C. C. Chang, and M. S. Hwang, A virtual iage cryptosyste based upon vector quantization, IEEE Transactions on Iage Processing, vol. 7, no. 0, 998, pp [5] W. Bender, D. ruhl, N. Morioto, and A. Lu, Techniques for data hiding, IBM Systes J. vol. 35, no. 3-, 996, pp [6] W. Bender, F.J. Paiz, W. Butera, S. Pogreb, D. ruhl, and R. Hwang, Applications for data hiding, IBM Systes J. vol. 39, no. 3-, 000, pp Rerferences [] C. C. Chang, J. Y. Hsiao, and C. S. Chan, Finding optial least-significant-bit substitute in iage hiding by dynaic prograing strategy, Pattern Recognition, vol. 36, no. 7, 003, pp [] C. C. Thien and J. C. Lin, A siple and high-hiding capacity ethod for hiding digit-by-digit data in iages 3

(c)(d)(e) are secret iages Jet, Tiffany, and Boat. Table.

Tiff Boat LSB 0.785.765 36.5536 enetic Algorith [] 3.97 3.705 36.

87.539 8 8.3.3. 0.73 0.890 0.767 0.3695 0.38 0.89 6 Table.

Jet Tiff Boat LSB 0.777.73 36.656 enetic Algorith [] 3.767 3.

6 (a) (b) (c) (d) (e) Figure 3. Testing iages (a) and (b) are cover iages: Lena and pepper. (c)(d)(e) are secret iages Jet, Tiffany, and Boat. Table. The MSE between the cover iage Lena and the stego-iage Methods Jet Tiff Boat LSB enetic Algorith [] Dynaic prograing strategy [] OPAP [, ] Table. The MSE between the cover iage Pepper and the stego-iage Methods Jet Tiff Boat LSB enetic Algorith [] Dynaic prograing strategy [] OPAP [, ]

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