REVERSIBLE DATA HIDING USING IMPROVED INTERPOLATION TECHNIQUE

Transcription

1 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: REVERSIBLE DATA HIDING USING IMPROVED INTERPOLATION TECHNIQUE Devendra Kumar 1, Dr. Krisna Raj 2 (Professor in ECE Department HBTI Kanpur) 1,2Department of Electronics & Communication Engineering Harcourt Butler Tecnological Institute 1,2 Kanpur, Uttar Prades, India *** Abstract- Reversible Data iding is a process of iding information into a cover image. Tere are various tecniques used for iding secret data. Audio, video, image, text, and picture can be used as a cover medium in data iding. In images, Reversible Data Hiding (RDH) is a metod wic is very elpful in te recovery of original cover after te inserted message is extracted. Tis vital metod is broadly used as a part of medical imagery, law forensics and military imagery, were no distortion of te original cover is permitted. Due to being first introduced, RDH as pulled in impressive researc interest. In te last decade, reversible data iding (RDH) as received muc attention from te information iding community. RDH is a special type of data iding and data extraction tecnique and it ensures a lossless recovery of secret and cover data. Specifically, by RDH, not only te embedded message, te cover content can also be exactly extracted from te marked data. Index Terms-Reversible Data iding, Medical imagery, Law forensics, Military imagery, Data extraction, Secret data, Cover data. 1. INTRODUCTION Data iding is a tecnique tat conceals secret data into a carrier to convey messages. Digital images are one of te media tat is suitable to convey messages due to several reasons. Firstly, digital images are often transmitted over te Internet wic would arouse little suspicion. Secondly, te ig correlation between pixels provides ric space for data embedding. In images, Reversible data iding (RDH) is a metod wic is very elpful in te recovery of original cover after te inserted message is extracted. Tis vital metod is broadly used as a part of medical imagery, law forensics and military imagery, were no distortion of te original cover is permitted. Due to being first introduced, RDH as pulled in impressive researc interest. 2. LITERATURE SURVEY In te practical point of view, numerous RDH strategies ave developed in past few years. Fridric et al. [1] developed a general system for RDH. Wit te initial extraction of te compressible caracteristics of original cover and after tis compressing tem losslessly, we can spare space for embedding auxiliary data. On te basis of difference expansion (DE) [2] a more popular process works in wic te difference of all pixel groups is extended, e.g., multiplied by two, and ence te LSBs of te distinction are all-zero and can be utilized for te purpose of embedding messages. Histogram sift (HS) [3] is anoter procedure in wic space is conserved for embedding of data by means of adjusting te istogram bins of gray values. Te state-of-art strategies [2] [7] normally consolidated DE or HS to residuals of te image, e.g., te anticipated faults, to accomplis improved performance. A few efforts on RDH in encrypted images ave been accomplised. In [4], Zang makes te division of encrypted image into many blocks. Wit flipping 3 LSBs of te alf of pixels in eac block, room can be cleared for te inserted bit. Te information extraction and image recovery continue by discovering wic section as been flipped in one block. Tis procedure can be acknowledged by means of spatial relationsip in decrypted image. Hong et al. [5] improved Zang s process at te decoder side wit te furter exploitation of te spatial correlation utilizing a dissimilar estimation equation and side matc metod to accomplis quite lower fault rate. Tese two tecniques specified above depend on spatial correlation of original picture to get data. Tat is, te encrypted image ougt to be decoded first before information extraction. Zang [6] suggests a novel metod for separable reversible information concealing.in tis, te content owner initially encrypts te original uncompressed image by making use of an encryption key to generate an encrypted image. Now, te least significant bits (LSB) of 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3037

2 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: te encrypted image is compressed by te data-ider by means of a data-iding key to develop a sparse space to put in te extra data. At te end of te receiver, te data inserted in te generated space can be effortlessly regained from te encrypted image comprising extra data as per te data-iding key. In [7], Lixin Luo, Zenyong Cen, Ming Cen, Xiao Zeng, and Zang Xiong 2010 presented te paper Reversible image watermarking using interpolation tecnique. In tis paper tey proposed a reversible watermarking sceme based on additive interpolationerror expansion, wic features very low distortion and relatively large capacity. Different from previous watermarking scemes, we utilize an interpolation tecnique to generate residual values named interpolation-errors and expand tem by addition to embed bits. Te strategy is efficient since interpolationerrors are good at de-correlating pixels and additive expansion is free of expensive overead information. Essentially, te data embedding approac of te proposed reversible watermarking sceme, namely additive interpolation-error expansion, is a kind of Difference Expansion. Reversible data iding tecniques can be generally classified into two frameworks 1) Vacate room after encryption 2) Reserve room before encryption In te first system after encryption (VRAE), owner of te content first encrypts te genuine image by making use of a typical ciper wit te elp of an encryption key and ten vacate room for data iding. In te second framework, reserve room before encryption (RRBE), te content owner initially reserve sufficient space on original image and after tis transforms te image into its encrypted format wit te elp of te encryption key. Te process of data extraction and tat of image recovery are similar to tat of Framework VRAE. Clearly, general RDH algoritms are te ideal operator for te purpose of reserving room prior to te encryption and can be effortlessly used to Framework RRBE in order to get improved results. Tis process can acquire genuine reversibility, tat is, data extraction and image recovery does not contain any error. In tis paper, we propose a reversible watermarking sceme based on improved additive interpolation-error expansion, wic features very low distortion and relatively large capacity. Different from previous watermarking scemes, we utilize an improved interpolation tecnique to generate residual values named interpolation-errors and expand tem by addition to embed bits. Te strategy is efficient since interpolationerrors are good at de-correlating pixels and additive expansion is free of expensive overead information. Additive Interpolation-Error Expansion Essentially, te data embedding approac of te proposed reversible watermarking metodology, termed as additive interpolation-error expansion, is a type of DE. Altoug, it is not like te most DE approaces in two vital aspects [13]: 1) It makes use of interpolation-error, rater tan using interpixel difference or prediction-error, to embed data. 2) It makes te expansion of te difference, wic is interpolation-error, at tis point by means of addition rater tan bit-sifting. Initially, interpolation values of pixels are estimated wit te elp of interpolation tecnique, wic works by guessing a pixel value from its surrounding pixels. Ten interpolation-errors are obtained troug e x x (1) In te above equation, parameter x represents te interpolation values of pixels. Suppose LM and RM represent te corresponding values of te two peak points of interpolation-errors istogram and be formulated as 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3038 LM arg max ist e ee RM arg max ist e ee L M (2) Were, ist(e) is te number of occurrence wen te interpolation-error is equal to e and te parameter E represents te interpolation-error set. Witout loss of generality, assume LM<RM. Ten, we classify te interpolation errors into 2 sections: 1) Rigt interpolation-errors (RE): interpolation-error e satisfies e RM. 2) Left interpolation-errors (LE): interpolation-error e satisfies e LM. We can formulate te additive interpolation-error expansion as given and furter can find te expanded interpolation error expansion using equation 3 given below.

3 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: , 1,,, e sign e b e LM or RM e, oterwise e e sign e e LM LM RM RN (3) Were e is te expanded interpolation-error, b is te bit to be embedded, and sign(e) is a sign function defined as sign e 1, 1, e RE e LE In (5), te parameters LN and RN are defined as LN arg max ist e ele RN arg max ist e ere (4) (5) Usually, LM is a very small integer and in most cases 0, wile LN is a smaller integer tat wit no interpolationerror satisfying e= LN. Similarly, in most cases, RM is equal to 1 and RN is a larger integer wit no interpolation-error satisfying e= RN.After expansion of interpolation-errors, te watermarked pixels x" become x" x e (6) During te extracting process, wit te same interpolation algoritm, we can obtain te same interpolation values x and te corresponding interpolation-errors via e x" x (7) So tis metod offers a feasible image interpolation algoritm to obtain interpolation values and interpolationerrors and we can find te original pixels using tese values [9]. Interpolation-Error Different from te recent conventional scemes, we exploit interpolation error, te difference between pixel value and its interpolation value, to embed data. Tere are several reasons tat lift interpolation error to be a better alternative to inter-pixel difference or prediction error. Te reasons for using interpolation-error instead of inter-pixel difference in te proposed sceme can be summarized as two points. Te first one is tat interpolation-error requires no blocking wic can significantly reduce te amount of differences and in turn lessen te potential embedding capacity. As in tis sceme, we almost get all pixels as candidates to embed messages except some negligible marginal pixels, wic promise a larger embedding capacity. Te second one is tat interpolation-error exploits te correlation between pixels more significantly. Fundamentally, te feasibility of reversible image watermarking is due to ig inter-pixel redundancy or inter-pixel correlation existing in practical images. To maximize te capacity of image watermarking, we sould exploit te correlation of pixels to te greatest extent. Te proposed sceme utilizes te full-enclosing pixels to interpolate te target pixel, so te interpolation-error tends to be smaller, wic means tat we can obtain a iger capacity. Once te same LM, LN, RM, and RN are known, embedded data can be extracted troug 0, e LM or RM b 1, e LM 1 or RM 1 (8) Ten te inverse function of additive interpolation-error expansion is applied to recover te original interpolationerrors, 1,, 1 1, [, 1) ( 1, ] e sign e b e LM LM RM RM e, e e sign e e LN LM RM RN oterwise Finally, we can restore te original pixels troug x x e (9) 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3039

4 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: x (2 2j 1) are interpolated wit te elp of te already recovered pixels x(2 2 j ). Let x w x w x (10) w Were, w 1 90 Figure 1: (a) Formation of a low-resolution image x l from te ig-resolution image x ; (b), (c) interpolation of residual samples of ig-resolution; (d) te interpolation of te sample pixels [9]. Let us take a low-resolution image x l is downsampled straigt-way from an associated ig-resolution, 1 j M. x by x x (2i 1,2 j 1), 1 i N l Wit te reference to Fig. 3.2(a), te black dots stands for te pixels of x wile te missing pixels of x is l represented by te wite dots. Te goal of interpolation is to make te estimation of te missing pixels in igresolution x, wose size measure is 2N 2M, from te pixels in low-resolution x l, wose size is N M,. Te major problem of interpolation is ow to infer and make use of te correlation between te missing pixels and te neigboring pixels. Wit te interpolation algoritm under discussion, we partition te neigboring pixels of all missing pixels into two directional subsets tat are ortogonal to eac oter. For eac subset, a directional interpolation is made, and after tis, fusion of te two interpolated values is done wit an optimal pair of weigts to calculate x. We make te reconstruction of te igresolution x in two step. In te first step, te missing pixels x(2 2 j ) at te center locations clouded by four low-resolution pixels are interpolated. In te second step, te oter missing pixels x (2i 1,2 j) and w Te weigts w and 0 are determined to minimize 90 te mean square-error (MSE) of : 2 x : { w, w 0 } arg min E[( x ) ] 90 x w w (11) Ten, we discuss te details of te first step using above analogy. Referring to Fig. 1(b), we can interpolate te missing ig-resolution pixel (2,2 ) x i j along two ortogonal directions: 0 diagonal and 0 diagonal. Denoted by x (2 i,2 j ) and x (2 i,2 j ), te two directional interpolation results are computed as x xli j xli j x xli j xli j, 1 1, / 2, 1, 1 / 2 (12) Here, we take e and e to represent te interpolation errors in te corresponding direction. Let e 2 2 j x 2 2 j x 2 2 j e 2 2 j x 2 2 j x 2 2 j (13) Figure 2: LSB replacement of te overead information [9]. 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3040

5 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: Instead of computing te linear minimum mean squareerror estimation (LMMSE) estimate of x, we select an optimal pair of weigts to make x a good estimate of x. Te strategy of weigted average leads to significant reduction in complexity. Let x w x w x w w 1 (14) Te weigts w and w are determined to minimize te mean square-error of x, arg min 2 w w E x x (15) w w 1 According to abundant experiments, te correlation coefficient between e and e, wic influences te correlation between x and x from (12), ardly canges te PSNR value and visual quality of te interpolation image. So we can sow te weigts are e e e w, w 1 w (16) were ( e), ( e) are te variance estimations of e and e, respectively. Tey are related to te mean value of x(2 2 j ) tat we will discuss. From (13), we can see intuitively ow te weigting metod works. For instance, for an edge in or near te 0 diagonal direction, ( e) is iger tan ( e) so tat w will be less tan w ; consequently, x as less influence on x tan x, and vice versa. Referring to Figure 1(b), te mean value of x(2 2 j ), denoted by u, is estimated by te available low-resolution pixels around x(2 2 j) To balance te complexity of computation and te consistency of pixels, we compute as u x1 i j x1 i j x1 i j x1 i j, 1 1,, 1, 1, 4 4 (17) Ten we return to te computation of ( e) and ( e ), wic are te variance estimations of interpolation errors in te corresponding directions. Tey are computed as 3 1 e S k u 3 k e S k 3 u k 1 were te two sets are, 1,, 1, S xl i j x xl i j S xl i j x xl i j 2 2,,, 1, 1 (18) (19) We can use (14) (19) to obtain te estimations of te missing ig resolution pixels x(2 2 j ) and finis te first step. After te missing ig-resolution pixels x (2 2 j ) are estimated, te residual pixels x (2i 1,2 j) and x (2 2j 1) can be estimated similarly. Referring to Figure 1(c), te black dots represent te low-resolution pixels, te gray dots represent te estimated pixels in te first step, and te wite dots represent te pixels tat are to be interpolated. 3. PROPOSED METHOD OF IMPROVED INTERPOLATION-ERROR ESTIMATION Figure 3: Estimation of center pixel troug surrounding pixels using proposed metod Considering a 3 3 part of an image, wose pixel value varies from C (1, 1) to C (3, 3), ten te mean value will be obtained using te pixel values of te pixels at 0 0 and 90 0 respectively. Let x C(2,2) is to be estimated, wit estimated value x. 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3041

6 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: e x x (26) Figure 4: Estimation of center pixel troug surrounding pixels at angles 0 0 and 90 0 Te mean value of surrounding pixel of x, at 0 and 90 degree is calculated as u 1 C (1,2) C (2,1) C (2,3) C (3,2) 2 C (2,2) 6 (20) In te mean central pixel is also included once in 0 0 and oter in 90 0 directions. Defining average value in 0 and 90 degree respectively as 1 x0 C (2,1) C (2,2) C (2,3) 3 and 1 x90 C (1,2) C (2,2) C (3,2). 3 (21) Considering, S [ C(2,1),(2,3), x ] And S90 [ C(1,2),(3,2), x90] 0 0 Te variance in 0 0 and 90 0 direction is e0 S0k u 3 k e90 S90 k 3 u k 1 Te weigt in 0 0 and 90 0 direction is given by w and w Te estimated value of te pixel is obtained by (22) (23) (24) x w0x0 w90x90 (25) So now te improved estimated interpolation error will be Generally, for any pixel value B jassessing error e B B and afterward is estimated by means of j j j certain data can be inserted into te estimating error arrangement wit istogram sift, Ten, furter calculation of te estimating errors of black pixels is performed by surrounding wite pixels in wic modification may ave been made. After tis, oter estimating error sequence is developed wic can provide accommodation to te messages too. Moreover, we can likewise execute multilayer embedding plan by considering te canged B as original one wen required. Finally, te output is tat in order to exploit all pixels of B, 2 estimating error sequences are developed for embedding messages in all single-layer embedding metods. Error sequence data can be added using Histogram sift Divide te Histogram in two parts L and R and searc for igest point in eac part LM and RM. In general LM = -1 and RM = 0. Searc for zero in eac part and define as LN, RN respectively. In order to insert messages into positions wit an estimating error wic is equivalent to RM, move eac error value between RM+1 and RN-1 wit one step to rigt, ten, te bit 0 wit RM and te bit 1 wit RM+1 could be represented by us. Te process of embedding in te left part is same wit te exception of tat te direction of sifting is left, and te sift is acknowledged by removing 1 from te relating pixel values. Generating te Marked Decrypted Image: In order to develop te marked decrypted image X wic is buildup of A and B, te content owner of te content sould follow te under given steps. Step 1. Wit te elp of te encryption key, te owner makes te decryption of te image putting aside te LSBplanes of A. We can calculate te decrypted version of E E aving te embedded data by X ( k) E ( k) r ( k) (27) i, j i, j i, j And 7 i, j i, j k0 X X ( k) 2 k, (28) 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3042

7 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: Above te parameters binary bits of respectively. E j( ) k and X j( ) k represents te E j( k) and X j( k ), acquired troug (27) Step 2. Extract SR and ER in marginal zone of B. Wit te rearrangement of A and B to its original form, te plain image comprising embedded data is acquired. As we can notice tat te marked decrypted image X is indistinguisable to modified X wit te exception of LSBplanes of A. Meanwile, it maintains perceptual transparency compared wit original image C. More particularly, te distortion is proposed by means of two different manners: te embedding procedure by canging te LSB-planes of A and self-reversible embedding procedure by inserting LSB planes of A into B. Te initial step distortion is quite managed troug exploiting te LSB-planes of just A and te next part can give advantage from penomenal execution of current RDH procedures. Data Extraction and Image Restoration After te generation of te marked decrypted image, te owner of te content can moreover extract te data and makes te recovery of original image. Te procedure is basically like tat of typical RDH tecniques. Te below describes te outlines te particular steps: Step 1. Make te Recording and decryption of te LSBplanes of A as per te data iding key; extract te data till reac te end label. Step 2. Extract LN, RN, LM, RM, LP, RP, R b, x and boundary map from te LSB of marginal area of B. After tis, scan B to undertake te below mentioned steps. Step 3. If R b is equal to 0, wic means no black pixels participate in embedding process, go to Step 5. Step 4. Calculate estimating errors e jof te black pixels B belongs to [1, 254], recover te estimating B j. If j error and original pixel value in a reverse order and extract embedded bits wen e jis equal to LN, LM (or LP), RM (RP) and RN. Else, if B j {0,255}, refer to te corresponding bit b in boundary map. If b=0, skip tis one, else operate like B j [1,254]. Repeat tis step until te part of payload R is extracted. If extracted bits are LSBs of b pixels in marginal area, restore tem immediately. Step 5. Make te calculation of estimating errors e jof te wite pixels B j, and extract embedded bits and carry out te recovery of wite pixels in te same way wit Step 4. In te event of te extracted bits are LSBs of pixels in marginal zone, restore tem at once. B Step 6. Carry on wit Step 2 to Step 5 x-1 rounds on and merge eac extracted bit to develop LSB-planes of A. Tis sould be continued till we recover B perfectly. A sould be replaced wit Step 7. Marked LSB-planes of its original bits extracted from B in order to get original cover image C. In tis paper, a novel metod is proposed for RDH in encrypted images, for wic we don t vacate room after encryption as carried out in [16] [18], but reserve room before encryption. In te proposed tecnique, as a first step, we empty out room by means of embedding LSBs of some pixels into oter pixels wit a traditional RDH metod and ten encrypt te image, so te positions of tese LSBs in te encrypted image can be used to embed data. Not only does te proposed metod separate data extraction from image decryption but also acieves excellent performance in two different prospects: Real reversibility is realized, tat is, data extraction and image recovery are free of any error. For given embedding rates, te PSNRs of decrypted image containing te embedded data are significantly improved; and for te acceptable PSNR, te range of embedding rates is greatly enlarged as it as been discussed tat te smaller interpolation error will be advantageous as te embedding area increases on te cover image te results in better PSNR and increased embedding capacity. 4. EXPERIMENTAL RESULTS AND COMPARISON In te MATLAB simulation six public available standard images Airplane, Baboon, Barbara, Boat, Lenna and Peppers in pgm format is considered. Eac image is of size and of 256KB. Te objective criteria PSNR and SSIM are employed to evaluate te quality of marked decrypted image. te results are drawn for proposed metod at 0.05bpp and te original image, watermarked image and SSIM trends are sown. Results sow as te embedding rate (bits per pixel) is increased, te PSNR and SSIM values reduce. In te table 1, and 2 results for RDH process is sown in wic PSNR(dB), SSIM values are detailed. Again ere as te BPP increases, 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3043

8 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: te SSIM values are detailed. Again ere as te BPP increases, te SSIM and PSNR value decreases. (a) Original Image (b) Watermarked Image (c) SSIM Figure 5: Airplane (a) Original Image (b) Watermarked Image (c) SSIM (a) Original Image (b) Watermarked Image (c) SSIM Figure 6: Baboon (a) Original Image (b) Watermarked Image (c) SSIM (a) Original Image (b) Watermarked Image (c) SSIM Figure 8: Lenna (a) Original Image (b) Watermarked Image (c) SSIM 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3044

9 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: Table 1: PSNR for RDH process for different images (Proposed metod) Bit Per Pixels Image PSNR (db) Airplane Baboon Barbara Boat Lenna Peppers Table 2: SSIM for RDH process for different images (Proposed metod) Bit Per Pixels Image SSIM Airplane Baboon Barbara Boat Lenna Peppers Table 3: Boundary map for RDH process for different images (Proposed) Bit Per Pixels Image Lengt of boundary map Airplane Baboon Barbara Boat Lenna Peppers , IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 30

10 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: Cart 1: Comparison of PSNR vs BPP for baboon image. Cart 2: Comparison of SSIM vs BPP for baboon image. Cart 3: Comparison of Boundary Map vs BPP for baboon image. 2017, IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3046

11 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: On te basis of results obtained, it can be found tat tat te cosen interpolation metods ave deep impact on te over-all performance of te RDH process. And te interpolation values sould be predicted in suc a way tat te error value attains -1, 0 and 1 as error, wic acieves iger embedding capacity. Tus inclusion of actual value of pixel in te mean value calculation wic is to be estimated, improves results significantly. Discussion on Boundary Map Boundary map in tis paper, is used for distinguising between natural and pseudo boundary pixels and its size is critical to practical applicability of proposed approac. Table 3 sows te boundary map size of six standard images. In most cases, no boundary map is needed. Even for Peppers image, te largest size is 831 bits (wit a large embedding rate 0.5 bpp by adopting embedding sceme 4 rounds) and te marginal area ( 512*4*4 bits) is large enoug to accommodate it. 5. CONCLUSION Reversible data iding in encrypted images is a new topic drawing attention because of te privacypreserving requirements from cloud data management. Previous metods implement RDH in encrypted images by vacating room after encryption, as opposed to wic we proposed by reserving room before encryption. Tus te data ider can benefit from te extra space emptied out in previous stage to make data iding process effortless. Te proposed metod can take advantage of all traditional RDH tecniques for plain images and acieve excellent performance witout loss of perfect secrecy. Furtermore, tis novel metod can acieve real reversibility, separate data extraction and greatly improvement on te quality of marked decrypted images. 6. FUTURE WORKS 1. In te future more better interpolation tecnique can be explored. 2. A better image partition metod can be searced. 3. M-SSIM can be used as image quality measure index. 4. A relation between PSNR and SSIM can be modeled, as some relation exists between tem. 7. REFERENCES [1] J. Fridric and M. Goljan, Lossless data embedding for all image formats, in Proc. SPIE Proc. Potonics West, Electronic Imaging, Security and Watermarking of Multimedia Contents, San Jose, CA, USA,Jan. 2002, vol. 4675, pp [2] Z. N Y. S N. Ansar and S. We Reversible data iding, IEEE Trans. Circuits Syst. Video Tecnol., vol. 16, no. 3, pp , Mar [3] W. Zang, B. Cen, and N. Yu, Improving various reversible data iding scemes via optimal codes for binary covers, IEEE Trans. Image Process., vol. 21, no. 6, pp , Jun [4] L. Luo et al., Reversible image watermarking using interpolation tecnique, IEEE Trans. Inf. Forensics Security, vol. 5, no. 1, pp ,Mar [5] V. Sacnev, H. J. Kim, J. Nam, S. Sures, and Y.Q. S Reversible watermarking algoritm using sorting and prediction, IEEE Trans. Circuits Syst. Video Tecnol., vol. 19, no. 7, pp , Jul [6] Xinpeng Zang, Member, IEEE Reversible Data Hiding Wit Optimal Value Transfer IEEE Transactions On Multimedia, VOL. 15, NO. 2, FEBRUARY [7] X. Zang, Separable reversible data iding in encrypted image IEEE Trans. Inf. Forensics Security, vol. 7, no. 2, pp , Apr [8] Ce-Wei Lee and Wen-Hsiang Tsail A Lossless Data Hiding Metod by Histogram Sifting Based on an Adaptive Block Division Sceme 2010 River Publisers. [9] Xinpeng Zang, Reversible Data Hiding in Encrypted Image, in IEEE signal processing letters, VOL. 18, NO. 4, pp , April 2011 [10] K. Ma, Weiming Zang, X. Zao, Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption, Information Forensics and Security, IEEE Transactions on Vol.8, No.3, [11] W. Liu, W. Zeng, L. Dong, and Q. Yao, Efficient compression of encrypted grayscale images, IEEE Trans. Image Process vol.19, no.4, pp , Apr [12] Xiang Wang, Efficient generalized integer transform for reversible watermarking, IEEE Signal Processing Letters, Vol. 17, No. 6, June , IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3047

12 International Researc Journal of Engineering and Tecnology (IRJET) e-issn: [13] Memet Utku Celik, Lossless generalized LSB data embedding, IEEE Transactions On Image Processing, Vol.14, No. 2, February [14] D.M. Todi and J.J.Rodriguez, Expansion embedding tecniques for reversible watermarking IEEE Trans. Image Process., vol.16, no.3, pp , Mar [15] Vidyadar Gupta, and Krisna Raj. "An Efficient Modified Lifting Based 2-D Discrete Wavelet Transform Arcitecture." Recent Advances in Information Tecnology (RAIT), st International Conference on Recent Advances in Information Tecnology (RAIT-2012), Date of Conference:17-19 Dec Page(s):1-6 Print ISBN: INSPEC Accession Number: Conference Location :Allaabad DOI: /ICPCES Dec [16] Krisna Raj, and Vedvrat. "A Study of VLSI Arcitectures for 2-D Discrete Wavelet Transform." IEEE International Conference on Power, Control and Embedded Systems (ICPCES), 2010 International Conference Page(s):1-3 Print ISBN: INSPEC Accession Number: DOI: /ICPCES Dec , IRJET Impact Factor value: ISO 9001:2008 Certified Journal Page 3048