Research Journal of Applied Sciences, Engineering and Technology 7(13): 2612-2621, 2014 ISSN: 2040-7459; e-ISSN: 2040-7467 © Maxwell Scientific Organization, 2013 Submitted: May 05, 2013 Accepted: August 23, 2013 Published: April 05, 2014 Fuzzy Watermarking for Colour Images based on Fusion of Discrete Wavelet Transform (DWT) Technique and Histogram Stretching 1 Karrar Abdulameer Kadhim, 1Dzulkifli Mohamad, 2Tanzila Saba and 2Fatima Nayer 1 Faculty of Computing, University Technology Malaysia, Johor, Malaysia 2 College of Computer and Information Sciences, Prince Sultan University, Riyadh, KSA Abstract: This study presents a method of scheming colour image by using fuzzy watermarking through Discrete Wavelet Transform (DWT) and histogram stretching methods. This study aims further at achieving and developing robustness, imperceptibility and available capacity of cover image for the watermarked image, which can withstand against various attacks. Digital watermarking techniques are used to protect the copyrights of multimedia data by embedding secret information in the host media, for example, embedding in images, audios, or videos. Many watermarking techniques have been proposed in the literature to solve the copyright violation problems, but most of these techniques failed to satisfy both imperceptibility and robustness requirements. In this research, adaptive RGB colour image fuzzy watermarking technique is proposed. The host image is converted from RGB to YCbCr colour space to preserve imperceptibility and robustness, then, Cb component is extracted and partitioned into four quadrants. While the Histogram Stretching will be used to convert the Watermark image to 4-bits plan to preserve the capacity for watermarking. Keywords: 4-bits plan, Discrete Wavelet Transform (DWT) technique, fuzzy watermarking, histogram stretching, RGB colour image INTRODUCTION There is no doubt that using the internet has become ubiquitous in the 21st century and it is quite true that everyone feels it is an indispensable part for the future of business communication. Going by the systematic digital data attainment and its conversion for the contemporary purpose is quite simple. Hence, copyright is an unavoidable issue to protect intellectual contributions (Rehman and Saba, 2014; Neamah et al., 2014). Tsai et al. (2000) and Wu (2002) highlighted recently that, through analyzing the trend of studies in digital watermark for audio, image or video data; it could be seen that watermarking techniques are the only way to prove legal ownership. Watermarking algorithms can be classified based on the domain used for watermark embedding. Studies have shown that two popular techniques; spatial and transform watermarking techniques exist. Spatial domain watermarking techniques are useful for higher data embedding applications (Muhsin et al., 2014, Rehman et al., 2013). Transform domain watermarking techniques are suitable in applications where robustness is of prime concern. These techniques as proposed include, Discrete Cosine Transform (Chandramouli et al., 2001) Discrete Fourier Transform (Premaratne, 1999) Discrete Wavelet Transform; Discrete Hadamard Transform (Anthony et al., 2002); Contourlet Transform (Jayalakshmi et al., 2006) and Singular Value Decomposition (Mohan and Kumar, 2008) are some of the useful transformations for image processing applications. With the introduction of the JPEG2000 standard digital image, watermarking schemes that are derived from Discrete Wavelet Transform (DWT) are becoming a robust area attracting lots of attention. Nearly all watermarking schemes consider Discrete Cosine Transform known as DCT method of preference. A summary are available in Cox et al. (2008). Though, study result shows that DWT has the potentials of enhancing the strength of watermark against intentional and unintended attacks. The primary reason would be that the former JPEG standard depended on DCT and today using the creation of JPEG 2000, schemes according to DWT are widely attaining interest. Though, watermark robustness varies using the underlying changed algorithms’, provisions must automatically get to harden a watermark against attacks. The methods are, e.g. multiple embedding Nasir et al. (2008) and the use of Error Correction Codes (ECC) are being used to restore the embedded watermark. DWT is basically a recent method used in place of an image but in a new time and frequency scale. The basic function of DWT is decomposing the input signal to multi-resolutions. “DWT can be used to decompose input signal that poses as image into low frequency Corresponding Author: Karrar Abdulameer Kadhim, Faculty of Computing, University Technology Malaysia, Johor, Malaysia 2612 Res. J. Appl. Sci. Eng. Technol., 7(13): 2612-2621, 2014 (LL) and high frequency (HL, LH and HH). HL here means the horizontal detail, while LH stands for the vertical detail and HH for the diagonal part. The lowest frequency band which serves as the optimal approximation of the original image, is influenced using the DWT decomposition progressions technique which represent the maximum scale and distinguishing degree” (Santhi et al., 2008). The main aim of this study work is to come up with a proposed method of scheming color image by using watermark through Discrete Wavelet Transform (DWT) and histogram stretching methods. This study aims further at achieving and developing robustness, imperceptibility and available capacity of cover image for the watermarked color image, which can withstand against various attacks (Rehman and Saba, 2012; Elarbi-Boudihir et al., 2011). PROBLEM STATEMENT Immediate digital image watermarking is certainly an urgent requirement for several today’s programs such as digital cameras and smart phone cameras. Evaluating two watermarking techniques, transform domain watermarking approaches require greater computational complexity over spatial domain techniques (Kougianos and Mohanty, 2011). This could further be as a result of forward and inverse changes in the transform-domain watermarking approaches. However, it is common to understand that, there are difficulties with spatial-domain techniques. For instance, high embedding errors in ISB bit-planes result in many researchers employing low-order bit-planes for instance LSB for data hiding (Maity et al., 2007). However, the lower-order bit-planes techniques does not contain visually significant information so, the PROBLEM BACKGROUND embedded watermark may be simply corrupted or transformed by unauthorized clients without affecting It is generally believed that, many studies centers on visual effects. Abolghasemi et al. (2010) for instance on the development of watermarking schemes for recommended a technique using co-occurrence matrix grayscale images than color images (Liu and Chou, and bit-plane clipping that could find out the hidden 2007; Saba and Rehman, 2012). Consequently, data in LSB. available records show that, information hiding has Studies have revealed the existence of several been an important research area in recent years. diversities of attacks against watermarking methods. However, the techniques to help address the issue of For example Basu et al. (2010), experimental unauthorized copying, tampering and multimedia data investigations showed that watermarking approaches in delivery through the internet require urgent attention. the past were prone to several kinds of malicious Information hiding techniques currently involves attacks. Consequently, in order to identify the merely the steganography and digital watermarking. ownership of the digital media, they had to be made Currently, digital watermarking schemes unavailable to extract the embedded watermark (s). concerning the data taken into account through getting Additionally, several attacks against watermarking rid of might be categorized as blind and non-blind schemes may be too complex to model (Cox et al., approaches. In non-blind watermarking approaches, 2008). Consequently, a universal watermarking both data for actual host image and understanding approach that could withstand several kinds of attacks statistics about watermarked image are known inside and, concurrently, satisfies the conventional as well as the amount of watermark recognition and extraction the embedding capacity needs getting a minimal (Tao and Eskicioglu, 2004). In contrast, in blind complexity isn't discovered yet. In this case, approach finding the watermark and not mention for the approximation approaches may be used to have the original image is preferred (Al-Otum and Samara, ability to discover the possession in the attacked 2010). You find several difficulties regarding the blind watermarked image with low computational watermarking approaches. On one hand, high complexity. The best way to develop a solution that effectiveness of blind watermarking may also be could withstand against various kinds of attacks is proven. Therefore, a completely new technique referred lacking the knowledge of their exact actions. to as semi-blind watermarking was introduced. Within Ibrahim and Kuan (2011) employed watermarking this kind of watermarking approach, only the original technique using DWT and encryption canny edges, so watermark or perhaps the watermarked multimedia as to include an watermark image include the cover statistics are known (Tao and Eskicioglu, 2004; Shieh image. Through the use of this method researcher was and Athaudage, 2006). able to get acceptable results for PSNR and NCC after An issue surrounding using the internet today is shedding a number of potential attacks. The main users "stealing" other individual’s images and taking requirements for watermarking are: Imperceptibility, advantage of them on the web site without permission. Robustness and capacity. Managed researcher attention It's impossible to prevent someone from installing imperceptibility and robustness, but neglected capacity, images out of your web pages. If you are an artist, so were some of the extracted watermark pictures after you'll most likely wish to safeguard your images by the attack prone to damage because the size of watermarking them. watermark image. Therefore, it is necessary to attention 2613 Res. J. Apppl. Sci. Eng. Technol., T 7(13)): 2612-2621, 2014 2 Fig. 1: Embbedding Stage imperceptiibility and robustness and capaccity simultaneoously to get th he best results extracted afterr a number off potential attaccks. ensure that the embeedding processs takes into acccount the main goals of waatermarking, (ii.e., imperceptiibility, robustnness and capaciity). Requantization imaage: One of the most impportant factors in informationn hiding is thee size for the hidden h The reesearch tactic of the proposeed watermarkiing data, which w is an acctive factor in the issue of hiding h method cann be explained d in Fig. 1. Thhis approach is to capacitty and PSNR R ratio. Reduciing the size of o the 2614 RESERCH METHODOL M OGY Res. J. Apppl. Sci. Eng. Technol., T 7(13)): 2612-2621, 2014 2 (1) (a) (b) (c) Fig. 2: Imagge quantization, a: Is the dark im mage, b: Is the ligght imagge, c: Is the quan ntized image (a) (b)) Fig. 3: Lenaa image has beeen converted, a: 0-255 range, b: 015 raange hidden leaads to increassing the efficciency of hidiing technique (Bauschke, 2003). Because the daata compressioon is not the main issue off our work, daata compressioon will not be used in details or as a professionaal stage. Imaage requantizzation or imaage normalizattion is generallly used for imaage enhancemeent by histograam stretching. In imaage, pixel valu ues vary from (0-255), ( while in some otheer images thesse values are condensed inn a partial rangge of original range (0-255). Sometimes, the t pixel valuees are near zeero value which means imaage will be in the dark view w. While imagee view is in ligght are if pixell values are neaar (255) valuess. The im mage requantizzation is used to t restretch pixxel values intto the origin nal range (0--255). Figure 2 illustrate im mage quantizattion where a: is the dark imagge, b: is the ligght image and c: c is the quantiized image. The prrocess of requaantization can be performed by next equattion” Histograam Stretching”” (González and a Woods, 20008): n obtained value after strettching, where, I new is the new mage pixel value before strettching, I old iss the original im Range is the new range that im mage value will w be M is the maxiimum and minnimum convertted to, Max, Min values pixel before stretching, s startt is the new sttarting values, which is in ouur case equal too zero. mage stretchingg is used essenntially for convverting Im values from a range to t another. Thiis research atteends to utilize image equaliization in ourr work in oppposite mannerr, i.e. convertinng the values frrom (0-255) raange to (0-15) range. Valuess of this rangee will need onnly (4) bits forr representatioon, as shown in Fig. 3, Illuustrate Lena im mage has been converted intoo two Ranges which, w a: 0-255 range, b: 0-115 range. The 0-15 range is a dark u it in our work w which will w be image, which will use i next step (Im mage Compression). useful in Image compression:: From the passt stage, imagee pixel values are convertedd from (0-2555) range into (0-15) range. Thus, convertted values neeed only (4) bitt from each byyte to represennt them, whichh means a hallf byte will bee empty for alll image bytess. In this case, each bytes will w combinedd into one dim mensional arraay that leads too get the half siize for the sam me image. Affter the stage for fo apply histoggram equalizattion to changee the level for image from (0--255) to (0-15)) level, as show wn previously,, now will com mpress the imaage by changee the array for this image to one dimennsional array to t be ensure compress c the image to a half h of originaal image. As shhown in Fig. 4. CbCr color spaace: The reseaarchers Converrt RGB to YC are using YCbCr color c space recently r becauuse of h YCbCrr color space is more robuust and has higher imperceptibility than RGB color sppace (Khalili, 2003). 2 mponent and CbCr Y reprresents the luuminance com represeent blue andd red chromiinance compoonents respecttively. In otherr hand RGB coolor space reprresents Fig. 4: Com mpress image by change to one diimensional arrayy 2615 Res. J. Apppl. Sci. Eng. Technol., T 7(13)): 2612-2621, 2014 2 (a) Fig. 6: Extracting E Y, Cbb, and Cr components invisibiility to HVS, Fig. F 6 shows extracting e Y, Cb C and Cr com mponents indiividually. Thee purpose of image partitiooning is findinng the best part p for embeedding waterm mark image, beefore applyingg DWT on thee part. One off these parts will w be chosen for embeddingg. The part whhich has the highest h impercceptibility reprresents the beest part for embedding thhe watermark. The experieences for the proposed p methhod prove the Cb is the best part for em mbedding to be ensuring higher h imperceptibility. Figuure 6 illustrate extract Cb partt. (b) Fig. 5: RGB B colour to YCbC Cr colour image red, greenn and blue ch hannels respecctively. Figuree 5 shows connverting host image from RGB R to YCbbCr color space. The main difference d betw ween YCbCr and a RGB colorr space is that YCbCr color space represennts the color as brightness with two moore color signals which are Cb C and Cr, wh hile RGB represents the colorr as red, green and blue. Nex xt Eq. 2 showss transformatioons between RGB R color space and YCbCr color c space (Chhai and Bouzerdoum, 2000): c image:: Discrete Wavelet W Apply DWT for cover transforrm (DWT) is a mathematical tool for hierarchhically decompposing an imagge (Hong and Hang, 2006). The watermarkk image is connverted into levvel (0mensional arraay, as 15) annd converted into one dim mentionned previouslyy, will read thhe cover imagge and apply DWT D algorithm m to split the image to four bands to mainntain the imperrceptibility. Thhe select quadrrant of host im mage decompposed by usinng discrete wavelet w transforrm (DWT), DWT D producedd the frequencyy subbands is i LL, HL, LH and HH bands. The HH bannd will be seleected to embeedding the waatermark imagge. As shown in Fig. 7 that illustrates how w to apply DW WT on the hosst image. dded waterm mark image in cover im mage: Embed Embeddding is hiding secret image or private labeel or a video or o a specific teext inside the cover c image without w significcant distortionss on the coverr image. Embeedding providees a sure wayy to determinee the owner of o the image, one of the most m importannt characteristtics of ( (2) m Embeddding is impercceptibility. Theere is lots of method to em mbed; the reesearch will use u one of them to C componen nts: Partitioninng is dividingg a Extract Cb embeddding the waterrmark image inn the cover image as particular image into multiple m parts; in general, the t will desscribe that. Affter converting cover image innto four Bandss using purpose of o image partitioning is changing the t DWT algorithm a and selection s the HH H band as shoown in representattion of image to be more eaase in processiing Fig. 8 and dependinng on the sizee of the wateermark and analyyzing. In prroposed technique, the Cb C image to ensure impperceptibility annd robustness. After componentt extracted fro om host imagge to preserve thhe 2616 Res. J. Apppl. Sci. Eng. Technol., T 7(13)): 2612-2621, 2014 2 Fig. 7: Applly DWT on the host h image Fig. 8: Recoonstruction YCbCr convertingg image wateermark to leevel (0-15) and a compressed it to a half from f original size s by changee it to one dim mensional arraay from 0 and 1 as previoussly mentioned, the next steep is embeddiing a watermaark image insidde the cover im mage in HH bannd, equation 3,, is the formulla for embeddiing which are WI is the outpput of the watermarking systtem, H be the host image, b is an approppriate scaling factor (Sulonng et al., 20112; Hajisami et e al., 2011; Sin ngh et al., 20099): WI = H + b * W ( (3) E Stage Fig. 9: Extract space to get final watermarked image form m. The followiing Fig. 8 illusttrates reconstruuction process.. Extracct stage: Extrraction stage is to separatte the waterm mark image of the cover imaage without caausing any siggnificant impacct on the waterrmark image and a the cover image. i The folllowing schem me illustrates how h to extract the watermarkk image from cover c image (Fiig. 9). where, WI = Wattermarked imag ge H = The value of host image i b = The scale factor (0 0.005) W = The watermark im mage (Binary) Wavelet reconstruction r n (IDWT): To return the imaage back from the frequency y domain to thee spatial domaain, Inverse Diiscrete Waveleet Transform (IDWT) ( applieed. All new frequency f sub-band coefficiients of selectted quadrant will w be reconstrructed using ID DWT. Both hiigh and low paass filter will inverse selecteed quadrant form frequency to the spattial domain with the sam me decomposiition level. Th his process will w generate one o watermarkked quadrant. b component:: In this stage, after Apply DWT for Cb mage will connvert RGB coolor to readingg the cover im YCbCrr like do it in embedding staage and then extract e Cb andd apply DWT to t extract wateermark image pieces as will shown that inn details. Applyy DWT algoritthm to split thhe image to foour bands. Thee select quadrrant of host im mage decompoosed by usingg Discrete Wavelet W Transfoorm (DWT), DWT D producedd the frequencyy subbands is i LL, HL, LH H and HH bannds. Then seleect the HH subb-band to get watermark w piecces. Reconstru uction YCbCrr: Finally, the quadrants q will be combined again to get YCbCr Y color space watermarkked image, theen YCbCr willl be converteed to RGB collor s illustratees how Extracct watermark image: This stage to extrract the pieces of watermark image from thhe HH 2617 Res. J. Apppl. Sci. Eng. Technol., T 7(13)): 2612-2621, 2014 2 sub-band of o cover image. After read the t watermarkked image andd apply DWT to division intto four sub-baand then select the HH sub b-band, will get g the pieces of ng et al., 2012;; Hajisami et al., a watermarkk image (Sulon 2011; Singgh et al., 2009) by the followiing formula 6. W = (W WI – H)/b (44) where, m n is the im mage size of watermark w andd host image, I (i, j) is the Host H image piixel value, K (i, ( j) is the Waatermark imagee pixel value, MSE M is Mean Square S Error foor (I, K). Robusttness measurring: To meaasure the sim milarity betweeen original watermark w im mage and exttracted waterm mark correlationn used as the foollowing Eq.7: where, W = The Watermark im mage (Binary) WI = The Watermarked image H = The Value of Hostt image b = The Scale factor (0 0.005) (7) Inverse convert c imag ge: After get the pieces of watermarkk image from the t HH2 sub-bband will inverrse the requanntization to co onvert the leveel for watermaark image from m (0-15) levell to original leevel (0-255) and a ensure to enhance e the im mage after convvert it. Equatioons 5, illustratte the convertt watermark im mage to (0-2555) level (Gonnzález and Woo ods, 2008): ( (5) where, Xold Xnew R Start v before coonvert level = The original value = The current vaalue, after convvert the level = The current raange (15) = The byte will be start for thee image (0) r n (IDWT): To return the imaage Wavelet reconstruction back from the frequency y domain to thee spatial domaain, ( applieed. Inverse Diiscrete Waveleet Transform (IDWT) All new frequency f sub-band coefficiients of selectted quadrant will w be reconstrructed using ID DWT. Both hiigh and low paass filter will inverse i selecteed quadrant form frequency to the spattial domain with the sam me his process will w generate one o decomposiition level. Th watermarkked quadrant. uring: Peak Signal S to Noise Impercepttibility measu Ratio (PSN NR) used to measure the quality betweeen original and a watermark ked image, inncreasing PSN NR related to the quality of o reconstructted watermarkked P formulaa: image. Thee following is PSNR ( (6) W i j are the pixel values at the where, W i j and W' positionn (i, j) of the original and the exttracted waterm mark by that 1 ≤ (i, j) ≤ 32, resspectively. RESULT TS AND DISCU USSION In this section results are presennted of the prooposed waterm marking based on Discrete Wavelet Trannsform (DWT)). The evaluatioon of these resuults presented in this chapterr by using sttandard imagees which are Lena, Baboonn, pepper, Tiffaany and Airplaane as a host im mages. Thhe results will get g it from embbedding a wateermark image in i standard RG GB colour imaages which are Lena, Baboonn, pepper, Tiffa fany and Airplaane images. Thhe host image are transform med into frequuency domain using Discrette Wavelet Traansform (DWT T), DWT resullted to four suub-bands frequencies which are a LL, HL, LH L and HH. Onne watermark image has beeen embedded inn subband (H HH) of host im mages. Thhe scale factoor (b) is useed to increasse the robustnness of waterm marking and (bb = 0.005) hass been chosen for it. The sizze of the host images i is (5122×512) byte annd the size for watermark image i is (128× ×128). The cooming figures illustrate the steps of embeedding result implementation i n before the attacks for seelected images. Thhere are a lot of o researchers prefer use YC CbCr , becausee the YCbCrr give more robust and higher h imperceptibility in embedding e stage from RGB B color space (Khalili, ( 2003)), Y is the lum minescent compponent represeents and CbCrr represents coolor componentts blue and reed, respectiveely. While RGB color space represeents the Red, Green G and Blue,, respectively. Apply DWT on CB components: The high freqquency componnents are usuaally used for watermarking since the hum man eye is leess sensitive to changes in edges (Kashyyap and Sinhaa, 2012). We will w apply DW WT to split the Cb componeent to four sub--band which arre LL, HL, LH H and HH bandds, then chose the t HH sub-baand for imbeddding the waterm mark image. The T Fig. 10 illuustrate the resuult after apply DWT D on Cb paart for Lena im mage. Thhere are a lot of researcherss prefer use YCbCr, Y becausee the YCbCrr give more robust and higher h 2618 Res. J. Apppl. Sci. Eng. Technol., T 7(13)): 2612-2621, 2014 2 To be all the byte for new imagge use four bit and useless four as we shhown in Fig. 11 illustrate the result after chhange the leveel to (0-15) thee image will be b like dark. Affter requantizattion watermarkk image, now itt has 4 bits in each byte forr the new wattermark imagee. The mark values intto one techniqque will changge the waterm dimenssional array as shown in the Fig. F 12. Fig. 10: Appply DWT on Cb component Fig. 11: Reqquantization imaage f RGB color imperceptiibility in embeedding stage from space (Khaalili, 2003). Y is the luminescent componeent represents and CbCr rep presents color components c bllue and red, respectively. While RGB color spaace represents the Red, Green n and Blue, resspectively. Requantizzation and compress wattermark imagge: One of thee most importan nt factors in innformation hidiing is the size for the hidden n data, which iss an active factor in the isssue of hiding g capacity annd PSNR rattio. Reducing the size of thee hidden is leaads to increasiing the efficciency of hiding techhnique. Imaage Requantizaation is geenerally useed for imaage enhancemeent by histograam stretching. But in propossed methode will w use the histtogram stretchiing to change the t level for watermark w imag ge from (0-2555) to (0-15) levvel, that for com mpress the imaage to half of the original sizze. a This section s Testingg and evaluation before attack: will teest and evaluaate the results for the prooposed methodd in this researrch depending on two criteriaa such as, PSN NR and NCC. In the begginng, the researcch will test thhe watermarkked image which w results from embeddding the waterrmark image inn Cb componeent for the covver image depending on PSNR R. Ass mentioned in chapter three,, the (PSNR) used u to measurre the quality between b originnal and waterm marked image, where, the increasing i in PSNR lead to t the quality of reconstructted watermarkeed image. Therrefore, s this reesearch will measure the PSNR for several waterm marked images such as, Lenna, Baboon, Pepper, P Tiffanyy and Airpllane. Figure 12 explainn the waterm marked imagees after appplying the embed e techniqque. Table 1 shows the PSNR P ratio foor the waterm marked images as shows in Fig. 13. Waterm marked imagge after attaccks: The folllowing lines will w explain the t result afteer applying various v attacks for three imaages which aree Lena and Baboon B mmon attacks are Gaussian noise, respecttively. The com Salt andd Peppers, etc.. The followingg sentences illuustrate results after attack. Fig. 12: Waatermark image in one dimensionnal array (a) (b) (c) (d) Fig. 13: Thee watermarked images, (a) Lenaa, (b) Baboon, (c) Pepper, (d) Tifffany, (e) Airplanne 2619 (e) Res. J. Appl. Sci. Eng. Technol., 7(13): 2612-2621, 2014 Table 1: Explain the PSNR ratio for watermarked image Abolghasemi, M., H. Aghaeinia, K. Faez and M.A. Mehrabi, 2010. Detection of LSB±1 steganography Images based on co-occurrence matrix and bit plane Lena clipping. J. Electro. Imag., 19(1). Baboon Pepper Anthony, T.S.H., J. Shen, S.H. Tan and A.C. Kot, 2002. Tiffany Digital image-in-image watermarking for copyright Airplane protection of satellite images using the fast hadamard transform. Proceeding of the IEEE Table 2: Summary of results International Geoscience and Remote Sensing Attacks Measurement Lena Baboon Symposium, 6: 3311-3313. Gaussian noise PSNR 21.120 20.930 NCC 0.9920 0.9951 Basu, D., A. Sinharay and S. Barat, 2010. Bit plane PSNR 26.030 25.980 Salt and pepper index based fragile watermarking scheme for NCC 0.9982 0.9978 authenticating color image. Proceedings of the First Sharpening PSNR 23.430 20.710 International Conference on Integrated Intelligent NCC 0.9018 0.9341 PSNR 27.850 22.730 Median filter Computing (ICIIC '10), pp: 136-139. NCC 0.9927 0.9971 Bauschke, H.H., 2003. Recompression of JPEG images Rotation PSNR 21.910 18.720 by requantization. IEEE T. Image Process., 12(7): NCC 0.9985 0.9928 843-849. JPEG compression PSNR 32.920 29.980 NCC 0.9982 0.9982 Chai, D. and A. Bouzerdoum, 2000. A bayesian approach to skin color classification in YCbCr In the Table 2, will explain the summary of the color space. Proceedings of the TENCON 2000, results of some a potential attacks which are: Gaussian Kuala Lumpur, 2: 421-424. Noise, Salt and Pepper, Sharpening, Median Filter, Chandramouli, R.R., G. Benjamin and R. Collin, 2001. Rotation and JPEG compression attacks with two A multiple description framework for oblivious measurements which are PSNR and NCC. watermarking. Proceedings of the Security, Watermarking and Multimedia, 4314: 585-593. CONCLUSION Cox, I.J., M.L. Miller, J.A. Bloom, J. Fridrich and T. Kalker, 2008. Digital Watermarking and This study has presented watermarking approach Steganography. 2nd Edn., Morgan Kaufmann embedded with discrete wavelet transform and high Publishers. frequency sub-band (HH1). This fusion is effective for Elarbi-Boudihir, M., A. Rehman and T. Saba, 2011. watermarking as human eye is not able to detect edge Video motion perception using optimized Gabor changes (Kashyap and Sinha, 2012). The high filter. Int. J. Phys. Sci., 6(12): 2799-2806. frequency areas are more imperceptibility than low and González, R.C. and R.E. Woods, 2008. Digital Image middle frequency areas and less sensitive to the human Processing. Prentice Hall, NY. visual system, but it is low robustness. That is the main Hajisami, A., A. Rahmati and M.B. Zadeh, 2011. idea of this thesis, which is how to embed in this subWatermarking based on independent component band without low robustness for embedded. analysis in spatial domain. Proceedings of the The problem solved by concerning (robustness) UKSim 13th International Conference on and finds the best quadrant for embedding. Histogram Modelling and Simulation, pp: 299-303. stretching equation used to compress the watermark Hong, W. and M. Hang, 2006. Robust digital image before embedding in the quadrant of the cover watermarking scheme for copy right protection. image, also the benefit for using histogram stretching IEEE T. Signal Proces., l2: 1-8. equation to ensure enhanced the watermark image after Ibrahim, R. and T.S. Kuan, 2011. Steganography extracting stage. The Histogram stretching equation Algorithm to Hide Secret Message inside an Image applies for watermark image to compress the image Computer Technology and Application, 2: before embedding to get more robustness and enhanced 102-108. the image after extract it. In addition to that watermark Jayalakshmi, M., S.N. Merchant and U.B. 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