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International Journal of Electrical & Electronics Engineering 20 www.ijeee-apm.com

IJEEE, Volume 2, Issue 4 (August, 2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426

IMAGE WATERMARKING AND ITSHARDWARE REALIZATION: A SURVEY

1Gaurav Gupta, 2Kanika Sharma1M.E Scholar, National Institute of Technical Teachers Training & Research, Chandigarh, India

2Assistant Professor, National Institute of Technical Teachers Training & Research, Chandigarh, India

Abstract—With the increase in the use of technology in themultimedia, the threat of piracy, tampering, customeridentification, forgery of digital rights and many more suchproblems have increased. For dealing with such situationsthe concept of watermarking is employed. A digitalwatermark is a digital signal or pattern inserted into a digitaldocument (text, graphics and multimedia presentations).Watermarking is one of the promising solutions for tamperdetection and protection of digital content. To enhance therobustness of the embedded information the patientinformation is coded by ECC (error correcting codes) likeRS (Reed Solomon), LDPC (Low Density Parity Check)and Convolution Codes. Software based watermarkingschemes are more prone to offline attacks due to the delaybetween image captured and embedding the watermark.Hardware based watermarking provides real timeembedding process where watermark is embedded at thesame time when image is captured. The goal of hardwareimplementation is to achieve low-power, high performance,real-time, reliable and secure watermarking system.Hardware can be realized using FPGA (FieldProgrammable Gate Array), DSP(Digital Signal Processors)or custom VLSI architecture. This paper surveys the variousdigital watermarking algorithms for real time applicationsinvolving still images.

Index Terms— Image Watermarking, SpatialWatermarking, Frequency Domain Watermarking,Invisible Watermarking, FPGA and Real timeWatermarking.

I. INTRODUCTIONDigital watermarking is the act of hiding information inmultimedia data (video, audio or images), for the purposesof content protection or authentication [1]. In digital imagewatermarking, the secret information (usually in the form ofa bit stream), the watermark, is embedded into an image(cover image), in such a way, that distortion of the coverimage because of watermarking is almost perceptuallynegligible. There are several characteristics of effectivewatermarks. For one, they must be difficult or impossible toremove. For another, they must survive common documentmodifications and transformations such as cropping andcompressing image files. They must also, in principle atleast, be easily detectable and removable by authorizedusers with such privileges (law enforcement agencies).Invisible watermarks should also be imperceptible, whilevisible watermarks should be perceptible enough todiscourage theft but not perceptible enough to

decrease the utility or appreciation of the document [2].Studies in the fields of data hiding are divided to somescopes. Figure 1 show these scopes in brief [3]. Data hidingfield generally includes both steganography andwatermarking. They have different types, too. Figure 1includes major types of them. Main categories aresteganography and watermarking. The word steganographyis derived from the Greek words “stegos” meaning “cover”and “grafia” meaning “writing” defining it as “coveredwriting”. In fact, steganography is the art and technique ofhiding a message in a carrier media [5]. Main difference ofsteganography and watermarking is the purpose of them.Steganography tries to hide the existence of message incarrier, while watermarking tries too add a message that canbe seen in a secret way.

Figure 1. Different methods of data hiding.

A. Need for WatermarkingThe art of secretly hiding and communicating informationhas gained immense importance in the last two decades dueto the advances in generation, storage, and communicationtechnology of digital content. Watermarking is one of thepromising solutions for tamper detection and protection ofdigital content. Digital watermarking attempts to copyrightthe digital data that is freely available on the World WideWeb to protect the owner’s rights. As opposed totraditional, printed watermarks, digital watermarks aretransparent signatures. They are integrated within digitalfiles as noise, or random information that already exists inthe file. Thus, the detection and removal of the watermarkbecomes more difficult. Typically, watermarks aredispersed throughout the entire digital file such that themanipulation of one portion of the file does not alter theunderlying watermark. To provide copy protection andcopyright protection for digital image and video data, twocomplementary techniques are being developed known asEncryption and Watermarking. One more method for datahiding is which is closely correlated with watermarkingknown as Steganography. Steganography was basically away of transmitting hidden (secret) messages betweenallies. There are various data hiding techniques are availablefor security [4], [5].

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B. Characteristics of WatermarkingThere are the following important characteristics ofWatermarking [4]:(i) Transparency: The most fundamental requirement forany Watermarking method shall be such that it istransparent to the end user..(ii) Security: Watermarked information shall only beaccessible to only authorized parties. They only have theright to alter the Watermark content. Encryption can beused to prevent unauthorized access of the watermarkeddata.(iii) Ease of embedding and retrieval: Ideally,Watermarking on digital media should be possible to beperformed on the fly. The computation needed for theselected algorithm should be least.(iv) Robustness: Watermarking must be robust enough towithstand all kinds for signal processing operations attacksor unauthorized access. Any attempt, whether intentionallyor unintentionally, that has a potential to alter the datacontent is considered as an attack.(v) Effect on bandwidth: Watermarking should be done insuch a way that it does not increase the bandwidth requiredfor transmission. If Watermarking becomes a burden for theavailable bandwidth, the method fails.(vi) Interoperability: Digitally watermarked content shallstill be interoperable so that it can be seamlessly accessedthrough heterogeneous networks and can be played onvarious plays out devices that may be aware or unaware ofwatermarking techniques.C. Types of WatermarkingDigital watermarking techniques can be divided intofollowing categories:Spatial Domain Watermarking: Several differentmethods enable watermarking in the spatial domain. Thesimplest (too simple for many applications) is just to flip thelowest-order bit of chosen pixels. This approach is usedcommercially for journalists to inspect digital pictures froma photo-stock house before buying unmarked versions [3].This method of spatial domain interleaving is susceptible tonoise.Frequency Domain Watermarking: The image is firsttransformed to the frequency domain and then the lowfrequency components are modified to contain the text orsignal. [18]. Since watermarks applied to the frequencydomain will be dispersed over the entirety of the spatialimage upon inverse transformation, this method is not assusceptible to defeat by cropping as the spatial technique.However, there is more a tradeoff here between invisibilityand decodability, since the watermark is in effect appliedindiscriminately across the spatial image [3,6]. Manyauthors have proposed the protecting the ownership rightsthrough the watermarking [7, 8, 9, 10]. And also authorshave implemented adaptive watermarking in the DCTdomain [11-15]. Many authors have implemented theWavelet based watermarking techniques in the Waveletdomain [16-22].Reversible Watermarking: In reversible watermarkingstrategy the image restored after the watermark extraction,is identical to the original cover image, pixel by pixel andbit by bit. Reversible watermarking finds widespread use inmilitary and medical imagery, where distortion-freerecovery of the original image after watermark extraction isof utmost importance [5,24&25]. However in many cases,

in spite of using a reversible watermarking technique, bit-by-bit recovery of the cover image may be infeasible. Forexample, military communication often takes place overhighly noisy channels (e.g. over a temporary, low-bandwidth radio data-link setup in the battlefield or nearenemy territory). Research indicates that packet error rates(PERs) of such channels, can be as high as 30%. In suchscenarios, it might not be possible to correct all errors at thereceiver end, in spite of using error-correcting codes.Consequently, because of the residual error in the receivedimage, both the recovered cover image and the watermarkwould exhibit distortions after watermark extraction. Themain purpose of any encryption mechanism is to protect thetransmitted data from any unauthorized interception,whereas reversible watermarking algorithms deal with theauthentication of transmitted data [26-29]. Reversiblewatermarking is gaining more attention for the last fewyears because of its increasing applications inmilitarycommunication, healthcare, and law-enforcement.First reversible watermarking scheme was developed by[30]. They utilized modulo addition 256 to achievereversibility in their watermarking technique. Since thenmuch improvements have been done in this field. [31]Developed a reversible watermarking approach bymodifying the patchwork algorithm and using moduloaddition 256. [30, 31] suffers from salt & pepper noisebecause of the use of modulo addition 256. A reversiblewatermarking technique without using modulo addition 256was then introduced by [32]. It proposed the concept ofcompressing the least significant bit (LSB) plane of coverimage to make space for the watermark to be embedded.However, the embedding capacity of this approach waslimited. To improve the embedding capacity andimperceptibility of the watermarked image, [33], thenproposed another approach. A number of new techniques,extensions or improved versions of the earlier techniques,have been proposed in recent years. The improvement isprimarily based upon making a good imperceptibility versuscapacity tradeoff [5]. [34] discussed key requirements of thewatermark and classified reversible watermarking schemesinto three categories: data compression, differenceexpansion and histogram shifting. A single reversiblewatermarking scheme is discussed in each of thesecategories. Some major challenges faced by the researchersin this field are also outlined. There can be different ways ofclassifying the reversible watermarking schemes. One suchclassification of reversible watermarking techniques isgiven below [5, 24]:(i) Compression based(ii) Histogram modification based(iii) Quantization based(iv) Difference Expansion based(v) Modification of frequency domain characteristicsAll the above schemes will be discussed later in the paper.

Irreversible Watermarking: Through suitablewatermarking techniques, the protection of the data can beensured and one can know whether the received content hasbeen tampered with or not. However, watermarking cancause damage to the sensitive information present in thecover work, and thus at the receiving end, the exactrecovery of cover work may not be possible. SuchWatermarking schemes are categorized as Irreversible


Watermarking techniques. In some applications, such lossof information can be tolerated, like Video on Demand(VOD) applications.Visible and Invisible Watermarking: Visible Watermarksare more overt means of discouraging theft andunauthorized use both by reducing the commercial value ofa document and making it obvious to the criminally inclinedthat the document’s ownership has been definitivelyestablished. Invisible watermarks should also beimperceptible, while visible watermarks should beperceptible enough to discourage theft but not perceptibleenough to decrease the utility or appreciation of thedocument [2].Robust Watermarking: Watermarking is said to be robustwhen the watermark payload can be retrieved from theattacked watermarked image. The attack can be blurring,enhancement, sharpening, compression, cropping orresizing of image. Any attempt, whether intentionally orunintentionally, that has a potential to alter the data contentis considered as an attack. Robustness against attack is akey requirement for Watermarking and the success of thistechnology for copyright protection depends on its stabilityagainst attacks. A robust watermarking algorithm should beable to extract a good quality of watermark from thewatermarked image even after undergoing differentcommon image processing operations. Robustwatermarking schemes are used for proving ownershipclaims.Fragile and Semi-Fragile Watermarking: Fragilewatermarks are designed to be damaged by any changes toan image and changes to the image can be detected andlocated. Semi-fragile watermarks are designed to bedamaged by tampering but also to remain unchanged by aset of allowed operations that may include imagecompression. Compression tolerance is often obtained byexcluding all image data which are expected to be lostduring compression, from the watermarking process.Although this ensures that the watermark will be correctlyrecovered from an un-tampered compressed image, itallows an attacker to tamper freely with and data that hasnot contributed to the watermark. This means that semi-fragile watermarks that are compression tolerant may notalways be usable for image authentication.D. Applications of WatermarkingDigital Watermarking finds application in various fieldslike military communication, Electronic Medical Records(EMR), data hiding in medical images, Video onDemand(VOD) services, video broadcasting digital rights,authentication of valid users, copyright protection of thepublishers, online shopping and many more.The number of commercial download platforms isincreasing and the current success of music and audio bookstores shows the increasing acceptance of those businessmodels [35].In medical images, patients’ details and thedoctors’ views can be inserted into the medical images toform a comprehensive data bank. However, data hiding inmedical images, due to their specific requirements imposecertain constraints, which set some specific requirements.To preserve high quality, one may embed information in theregion of non-interest (RONI) [36, 37, 38].There are many more such applications of Watermarkingwhich will be discussed along with the Watermarking

schemes and the Hardware Implementation of suchtechniques for real time applications.

II. WATERMARKING ALGORITHMWatermarking algorithms can be classified according todifferent basis. One of the most frequently used algorithmsis reversible watermarking algorithm as it gives maximumprobability of exact retrieval of the watermarked image. Theapplications of reversible watermarking can be found in thefields where the information content of the image is verysensitive and minor changes or distortion in the retrievedimage may be very harmful. For example, the medicalimages are watermarked and then stored or transmitted foruse in EMR (Electronic Medical Record). In such a case,the details in the ROI (Region of Interest) should not bealtered at any cost. For this purpose, only losslesswatermarking algorithm should be employed. To muchextent, reversible watermarking is lossless. Such a situationis also prevalent for military applications. In this case alsoreversible watermarking is preferred. The pioneering workin the field of reversible or lossless watermarking could befound in [39, 40]. In [39], the bits to be overlaid will becompressed and added to the bit string, which will beembedded into the data block. [40] Reconstruct the payloadfrom an embedded image, and then subtract the payloadfrom the embedded image for lossless recovery of theoriginal image.

Difference Expansion: The pioneering work in this field isdone by [41]. A high-capacity, high visual quality,reversible data-embedding method for digital images ispresented in this work. This method can be applied to videoand audio as well. [41] Calculates the differences ofneighboring pixel values, and select some difference valuesfor the difference expansion (DE). The original contentrestoration information, a message authentication code, andadditional data (which could be any data, such as date/timeinformation, auxiliary data, etc.) will all be embedded intothe difference values. The redundancy in the image contentis explored for embedding the payload in the digital image.The amount of payload added to the image accounts for thevisual quality of the embedded image. The performance ofdata embedding algorithm can be measured by thefollowing parameters:(a) Payload capacity limit(b) Visual quality(c) Complexity of algorithmFor an 8-bits grayscale image with pixel pair (x, y), wherex, y ∈ Z, 0≤ x, y≤ 255, define their integer average l anddifference h as

= x +2 , ℎ= x − y (1)The inverse transform of (1) is given by (2).

= + ℎ + 12 ,= − ℎ2 (2)The reversible integer transform pairs are known as HaarWavelet Transform, or the S transform. The (x, y) and (l, h)


are one to one mapped. To restrict the values in the range[0, 255], the following conditions must be met:0 ≤ + ℎ + 12 ≤ 255 and 0 ≤ − ℎ2≤ 255 (3)Then expansion of h takes place to embed the watermark bitb. ℎ′= 2ℎ+ (4)To produce watermarked pixel( ′, ′) , a reverse integertransformation is performed on ℎ′ and l as shown byrelation (5).

′ = + ℎ′ + 12 and ′ = − ℎ′2 (5)During watermark extraction, at the receiver side, again thedifference between two adjacent (watermarked) pixels iscomputed. This time the watermark bit is extracted and thedifference number contracted or restored to its originalform. This allows the reversibility of the cover image aswell as lossless extraction of the watermark. As shown inthe results of [41], the PSNR of the retrieved image undernoisy channel decreases as the payload size (the amount ofdata embedded).Further work has been done to improve the performance ofthe existing algorithm. For the multilayer embeddingapplications the DE algorithm suffers from degradation inthe visual quality (in terms of PSNR) of the embeddedimage after the first layer embedding due to the use of largedifferences. Second, the new difference image has smallerembedding capacity than its predecessor. Each layer-embedding progressively decreases the correlation not onlyin the embedding directions but also of the neighborhood.Thus, multiple-embedding does not effectively exploit thecorrelation inherent in a neighborhood [42]. Third, thealgorithm can not keep its behavior smoothly because eachlayer embedding has its own embedding capacity limit. Thesudden dip in the capacity versus distortion curve for theDE methods around 0.5bpp is the effect of multiple-layerembedding [43]. For this purpose, a modified DE isproposed by [43, 44]. [43] Improve the visual quality of theembedded image by 6dB as compared to the DE algorithm[41]. The proposed RDE method [43] uses a transformationfunction to reduce the value of the expansion difference hfor obtaining a reduced expansion differenceℎ. Theproposed transformation function can be represented asℎ= |ℎ|, |ℎ| < 2|ℎ| − 2⌊ | |⌋ , |ℎ| ≥ 2 (6)The reduced difference expansion embedding is defined as:ℎ′= 2ℎ+ (7)To successfully restore the original difference value, abinary map (or Extraction Map) is created. The size of theExtraction Map is same as that of the number of pixel pairs.It should be noted that, the Extraction Map is considered asthe side information of the proposed RDE method.Therefore, the Extraction Map should be compressed andstored for later extraction of the embedded data and

restoration of the original image. For application ofmultilayer embedding, the Extraction Map used to extractthe (i-1)th layer data is embedded in the ith layer. That is,the user needs only to store the last-layer Extraction Map.Based on integer Haar wavelet transform, [44] proposes analgorithm that selects expandable differences under thesame selection threshold in two difference images andembeds the payload in two orthogonal embeddingdirections. This scheme greatly improves image quality.The algorithm performance is smooth and varies graduallywith the change of payloads.To improve the performance of the system in terms ofcomputation power, [45] proposed a method whicheliminates the need of the Location map being used in [41].The proposed method not only retains the embeddingcapacity but also removes both the location map and theLSBs of changeable differences which have not beenexpanded from the recovery information such that all of theembedding capacity can be used to embed user’s message.In addition, the embedding capacity of an image can befinely tuned according to the size of required space.Integer transform: The high hiding capacity can not beachieved only by difference expansion, so the compandingtechnique is introduced into the embedding process so as tofurther increase hiding capacity. The invertible integertransform exploits the correlations among four pixels in aquad. Data embedding is carried out by expanding thedifferences between one pixel and each of its threeneighboring pixels. The transform is proposed to calculatethe difference between one pixel and each of its threeneighboring pixels in a quad. The companding technique isintroduced so that the differences larger than or equal to thethreshold can also be expanded. Accordingly, the locationmap can be compressed into a very short bit stream tolargely increase the embedding capacity. [46] Proposed thistechnique.A 2X2 group of pixels in a gray scale image I is herereferred as a quad. A quad is denoted by q.= 1 23 4 , 1, 2, 3, 4 (6)A forward integer transform T(.) is applied to every quad of2X2 pixels as given by (7).1 = 1 + 2 + 3 + 442 = 1 − 23 = 1 − 34 = 1 − 4 (7)A forward integer transform is carried out on every quad togenerate three difference numbers that are expanded toembed three watermark bits. The inverse integer transformis given by (8). 1 23 4 = 1 23 41 = 1 + 2 + 3 + 442 = 1 − 23 = 1 − 34 = 1 − 4 (8)This process is composed of three parts:1) Companding: a quantized compression function, CQ, isrespectively applied to v2, v3 and v4;


2) Classification: each quad is classified into one of threecategories with the help of three outputs vQ1, vQ2 and vQ3;3) Data embedding: it is implemented based on the quad’scategory.

For extraction, every 2×2 adjacent pixels in the markedimage Iw are grouped into a quad in the same way as inembedding. T (.) is applied to each quad to get its

transformed one = 1 23 4 , where 1, 2, 3, 4 is

given by (9) if q belongs to class C1 else given by (10) if qbelongs to class C2. 2 = 2 + 23 = 2 + 34 = 2 + 4 (9)

2 = 2 22 + 23 = 2 32 + 34 = 2 42 + 4 (10)The extraction process consists of two steps:(a) Location map extraction(b) RestorationFinally (. ) is applied to reconstruct the original imageI.

Speed and complexity of the algorithm is an importantfactor to be considered in implementing such algorithm inreal time systems. For implementing such algorithm thedata must be processed as soon as it is coming in so thatdelay may not harm or disrupt the processing. For thispurpose the computational complexity should not be muchhigher which results in faster processing of the inputsignals. In the existing algorithms, there is a need to create alocation map which must be stored and compressed with alossless compression technique so that no informationmight be lost. This compressed location map is transmittedwith the image itself. A reversible watermarking schemebased on reversible contrast mapping (RCM) is proposed by[47], in which the location map is not needed. The schemedoes not need additional lossless data compression, and thecomputational complexity is extremely low for both dataembedding and extraction. This important feature makes itappropriate for real time applications. However, thismethod can embed only one bit into a pixel pair. Thus, in asingle pass embedding, its embedding rate can not exceed0.5 bit per pixel (bpp). In [48], the work is extended andproposes a novel reversible image watermarking schemebased on a generalized integer transform. The proposedmethod uses a block that contains n pixels, and (n − 1) bitsare embedded into each suitably selected block, where n is apositive integer. Comparing with [47], this method canprovide a higher bpp while giving a better peak signal tonoise ratio (PSNR). In addition, there are no datacompression steps in this method, which results in fast dataembedding and extraction.In general, the performance of a reversible imagewatermarking method is evaluated in two aspects: the

embedding capacity and the visual quality. More precisely,we expect to increase the embedding capacity as high aspossible while keeping distortion low. Data hiding schemeswith high embedding capacity have also been proposed by[49] which achieve embedding capacity as high as 1.85 bppin “Lena” image. Besides, by pre-estimating the embeddingdistortion, one can suitably select embeddable blocks sothat the visual quality of the watermarked image is wellguaranteed. Furthermore, extensive experiments show thatthe novel method performs better than some state-of-the-artalgorithms. While keeping the distortion low, [50] proposesa method to increase the embedding capacity. The integertransform is calculated based on the difference of adjacentpixels. It can be applied to a pixel block of arbitrary sizewith adjustable capacities. Moreover, to reduce thedistortion further, the integer transform is conducted byconditionally embedding data only when the estimateddistortion is acceptable. Another method to increase theembedding capacity based on 2D lifting Wavelet transformis proposed by [51]. This method presents the reversiblewatermarking algorithm which aims to increase embeddingcapacity by using the proposed block linking method,instead of the location map. This method which is based oninteger transform is applied on the detail coefficients sub-bands of 2-D lifting wavelet transform. The main advantageof this algorithm is that the block linking method is fasterand simpler compared to the location map proposed by [41]which need a complex loss less compression. This methodcan achieve high capacity for image watermarking whilepreserve good image quality.

Compression based: The technique of compression is alsoused in the already discussed algorithms. There are manymore such algorithms which employ compression forcompressing some of the bit planes of an image matrix formaking for embedding data. Lowest bit planes are altered sothat the distortion in the image is perceptually negligible. Ageneralization of the well known least significant bit (LSB)modification is proposed by [52] as the data-embeddingmethod, which introduces additional operating points on thecapacity-distortion curve. This is a spatial domain techniquefor watermarking. Lossless recovery of the original isachieved by compressing portions of the signal that aresusceptible to embedding distortion and transmitting thesecompressed descriptions as a part of the embedded payload.A prediction-based conditional entropy coder which utilizesunaltered portions of the host signal as side-informationimproves the compression efficiency and, thus, the losslessdata embedding capacity. In this method, the LSB of theimage data is replaced by a payload data bit per inputsample. If larger embedding capacity is required then two ormore bits are replaced according to the need. Duringextraction, these bits are read in the same scanning order,and payload data is reconstructed. A generalization of theLSB-embedding method, namely G-LSB, is employed in[52]. If the host signal is represented by a vector, the G-LSB embedding and extraction processes can berepresented as= ( ) + (11)= − ( )=− ( ) (12)


Where represents the signal containing the embeddedinformation, w represents the embedded payload vector of

L-ary symbols, {0,1, … , − 1}, ( ) =is an L-level scalar quantization function, and ⌊ ⌋represents the operation of truncation to the integer part.Obtain quantized pixel value ( ) and remainder ( ),where ( ) = − ( ). Then apply lossless compressionto the remainders. In [52], arithmetic coding is used.Concatenate the compressed remainders and the watermarkW to form the bit stream H. Convert H to L-ary symbolsand then to the quantized pixels. This gives thewatermarked image. In the embedding phase, the lowestlevels of the signal samples are replaced (over-written) bythe watermark payload using a quantization step followedby an addition. Extraction is carried out by L-levelquantization of watermarked pixels. This time theremainders are converted from L-ary form to binary form.After watermark extraction the leftover portion of thisbinary bit stream is decompressed to get back the originalremainders. The original cover image can be restored bit-by-bit when the recovered remainders are added to thequantized pixels.

Modification of Frequency Domain Characteristics: Insuch scheme, watermark is embedded in the spectralcomponents of the image. Spatial domain algorithm canhide large amounts of data, but the robustness of thealgorithm is Poor performance and can severely damage thewatermarking, compared with the spatial domainwatermarking algorithm, transform domain watermarkingalgorithm has high hidden, strong robustness, goodcompatibility [20]. Bit shifting is employed in spatialdomain for watermarking the image which suffered fromlow capacity and overflow or underflow problems. Theapproach proposed by [53] is especially efficient for audiosignals by employing the companding techniques typicallyused in telephony systems. The high capacity generated bythis approach can be attributed to the highly concentrateddistribution shape of the histograms of common audiosignals, which looks like a Laplacian distribution shapearound the zero point. This fact is especially suitable for thebit-shift operation of the companding technique because itmakes the companding error small enough to save morespace for the watermark bits. On the other hand, for usualaudio signals, the perceptional distortion is limited to aloudness difference which is attributed to amplified sampleamplitudes caused by bit-shift operations. However, naturalimages’ histogram is shaped differently. And bit-shifts onpixel values will cause much noticeable distortion toimages. These two facts make this companding and bit-shiftbased approach hard to be suitable for reversible imagewatermarking applications, although the capacityperformance is quite a desirable advantage of this reversibletechnique. This reversible watermarking approach proposedin [53] shows high capacity which is close to 1 bit persample for audio signals. But due to the disadvantage ofnatural images’ histogram, this approach is obviously notsuitable for images. A 2-dimensional integer DCT basedapproach is proposed in [54] to circumvent the problemsmentioned above and use the bit-shift operation ofcompanding technique successfully in reversiblewatermarking for images. [54] Choose AC coefficients inthe integer DCT domain for the bit-shift operation, and

therefore the capacity and the quality of the watermarkedimage can be adjusted by selecting different numbers ofcoefficients of different frequencies. To prevent overflowsand underflows in the spatial domain caused bymodification of the DCT coefficients, we design a blockdiscrimination structure to find suitable blocks that can beused for embedding without overflow or underflowproblems. We can also use this block discriminationstructure to embed an overhead of location information ofall blocks suitable for embedding. With this scheme,watermark bits can be embedded in the saved LSBs ofcoefficient blocks, and retrieved correctly during extraction,while the original image can be restored perfectly. First, the512x512 image is broken down into 8x8 blocks of pixels.Integer DCT is calculated for each block and thencoefficients are selected for embedding the payload dataaccording to twice try based structure for blockdiscrimination. Left shift the selected coefficients and insertthe watermark bits into the LSB positions. The coefficientsare modified now. Lastly apply inverse integer DCT toproduce the final watermarked image. The embedding andextracting process for 8x8 image block is shown in figure 2[54].

Figure2. Embedding and Extraction process for 8x8 image block[54].

In [54], the performance is compromised in high PSNRranges (approximately >40dB) compared with their goodperformance in middle and low PSNR ranges. Histogrammodification schemes [55] and [56] for reversiblewatermarking have good performance in high PSNR rangesbecause of the relatively low distortion of the histogramshifting operation, However, the PSNR dynamic rangesresulting from [55] and [56] are highly restricted by originalimages' pixel value histograms when the histogrammodifications are directly performed in the spatialdomain.[57] Proposes a reversible watermarking schemeusing histogram modification in the 8x8 integer DCTdomain. This proposed scheme exploits the high energyconcentrating property of integer DCT and allows finecoefficient selection for watermarking, and thus, showsequivalent or higher performance and wider quality (PSNR)ranges compared to [55] and [56]. The histogrammodification techniques in both [55] and [56] are directlyused in the spatial domain and the algorithms' performanceis determined by the pixel distribution of the original image.Using histogram modification in the integer DCT domain inlight of integer DCT's energy concentration property, andexpect to heighten the amplitude of the peak point P andthus increase the capacity. Similar to a float-point DCT, aninteger DCT has the energy concentration property, whichcan be used to improve the capacity of histogram


modification scheme. Unlike the float-point DCT, theinteger DCT is lossless and suitable for reversiblewatermarking. The coefficients of the integer DCT (of the8x8 pixel blocks) are calculated. Groups of coefficients arecalculated having a position (p, q) in all the 8x8 imageblocks. For example, coefficients having position (2, 3) inevery image block is grouped in G(2, 3). Similarly, G(p, q)is calculated for every pixel in the image block. In this way,64 coefficients groups will be created. Number of elementsin every coefficient group will be equal to the total numberof image blocks. For every coefficient group, a histogram iscalculated. In this way, total 64 histograms are calculated.For each coefficient group G(p, q), we embed thewatermark reversibly by histogram modification on itshistogram H(p, q)( ≥ 1, ≤ 8). The principle ofhistogram modification used in [55, 56] is used in [57] formodification of coefficient histogram H(p, q). Thetransform from spatial domain to integer DCT domainshould improve the whole algorithm's capacity and imagefidelity. A relatively simple scheme to store the overheadinformation is employed in [57]. Unlike “twice try” blockdiscrimination scheme used [54], [57] uses a comparativelyless complex scheme to store and process the overheadinformation. The original image can be divided into 16x16blocks and we pick out some blocks as overhead blocksaccording to a secret key and then replace the LSBs ofpixels in these blocks with the bits of the overhead. And theoriginal LSBs are embedded with watermark bits into thesaved space from the histogram modification technique.During extraction, we can use the secret key to find outthese blocks and extract the overhead information. Theproposed scheme [57] has relatively high capacity in thehigh PSNR range. Compared with other reversible imagewatermarking schemes [41, 54, 55, 56], this scheme showsequivalent or higher performance, and particularly,capability of fine adjustment of the watermarked image'squality (PSNR) by selecting different numbers ofcoefficients.Another approach for reversible and fragile watermarking isproposed by [58]. In this approach Discrete CosineTransform (DCT) is used for generating the coefficients ofthe 8x8 blocks of image. These coefficients are thenembedded with information for authentication at thereceiving end. According to the characteristics ofcompression transformation, coefficients of high absolutevalues are mainly localized in low-frequency domains;however, coefficients in high-frequency domains alwayshave relatively low absolute values. The watermark is thenembedded in the high frequency contents. The problem ofoverflow and underflow of pixel values is also handledsuccessfully. The results of [58] are shown in figure 3. Thequality of watermarked image figure 3(b) doesn’t obviouslydrop and its PSNR (peak signal to noise ratio) is 39.14dB.

Figure3. Experimental results of [58]. (a) Original image (b)watermarked image (c) the result of tampering detection afterJPEG compression (d) tampered watermarked image (e) the

result of tampering detection (f) the result after revision.

Then the image figure 3(b) is JPEG-compressed (qualityfactor is 90), after that the tampered regions in compressedimage are detected, as shown in figure 3(c), which is notrevised. The white parts in figure 3(c) are the tamperedregions after the watermarked image is compressed.Watermarks have been seriously damaged by observing thefigure 3(c), which has shown that the watermarks are quitefragile even when the quality factor of JPEG-compression isstill high. Then we put a “clock” pattern on the left-top offigure 3(b) to create a tampered image, as shown in figure3(d). Figure 3(e) is the corresponding image of tamperingdetection for figure 3(d). Figure 3(f) is the result of revisionfor figure 3(e) by using the revising method proposed [58](α =5 when the revision is made). Clearly, the detection oftampered regions is more accurate after the revising methodis used.In medical image applications, most of the proposedreversible watermarking methods are spatial-domainwatermarking schemes, such as histogram modification [59]and difference expansion based embedding scheme [60]. Toimprove the capacity and reduce the embedding induceddistortion, most of the schemes exploiting the ROI (RegionOf Interest) and RONI (Region Of Non-Interest) of themedical images. Unlike ordinary images, medical imageshave consecutive ROI and RONI areas, which offer benefitsfor watermark embedding algorithms. [61] Presents areversible watermarking scheme based on Integer DCT andDE. The energy threshold selecting scheme introduces anew way for choosing the ROI blocks of the medical image.Instead of the rectangle area selection [62] and block basedselection [63], the energy threshold is used to select the ROIblocks adaptively. The goal of using these methods is toachieve reversibility while minimizing the perceptionaldistortion. The Discrete Cosine Transform (DCT) has beenwidely used in the algorithm design of digitalwatermarking. As DCT is a transform of real numbers, theoutput of DCT transform is in real number form, which maycause some extra rounding off in image processing. IntegerDCT transform uses integers instead of regular floatingpoint numbers to fill the transformation matrix. Thetransformation core is an integer transform, which has nofloating-point calculations and get a high accuracy. The


core transformation can be completed through simpleaddition and shift operations, which can reduce thecomputing complexity. Integer arithmetic avoids accuracyerror, ensures the reversibility of coding, and solves thematching problem between encoders' and decoders' forwardtransform and inverse transform; this characteristic has beenused in some watermarking schemes [54]. The schemeproposed by [61] involves dividing the image into 4x4blocks and then calculating integer DCT coefficients ofeach block. Energy of each block is calculated and the blockhaving energy greater than specified threshold energy isselected for embedding the secret bits into it. Finally applyinverse integer DCT of each block. This gives the finalwatermarked image. the middle frequency coefficients areused to embed the secret bits, because the DE embeddingneeds 2 coefficients to carry 1 bit, the coefficient number 8and number 9 are chosen for embedding as shown in Figure4. Low frequency components are not used for embeddingpurpose because they greatly affect intensity of the image,hence disturbing them may seriously induce perceptualdistortion. High frequency components may be used forembedding secret bits but it hinders the image processingoperations. The above mentioned algorithms do not standsuitable for applications demanding larger embeddingcapacities. The embedding capacity in all the abovementioned algorithms [54, 57, 58 & 61] is found to be lessin comparison to [64]. Integer wavelet transform is used fortransforming the image into frequency domain in [64]. Thisalgorithm hides data into one (or more) middle bit plane(s)of the integer wavelet transform coefficients in the middleand high frequency sub-bands.The above mentionedalgorithms do not stand suitable for applications demandinglarger embedding capacities. The embedding capacity in allthe above mentioned algorithms [54, 57, 58 & 61] is foundto be less in comparison to [64]. Integer wavelet transformis used for transforming the image into frequency domain in[64]. This algorithm hides data into one (or more) middlebit plane(s) of the integer wavelet transform coefficients inthe middle and high frequency sub-bands.

.Figure4. 4x4 matrix of coefficients.

It can embed much more data compared with the existingdistortion less data hiding techniques and satisfy theimperceptibility requirement. The image histogrammodification is used to prevent grayscales from possibleoverflowing. Spatial domain algorithm can hide largeamounts of data, but the robustness of the algorithm is Poorperformance and can severely damage the watermarking,compared with the spatial domain watermarking algorithm,transform domain watermarking algorithm has high hidden,strong robustness, good compatibility [65]. Waveletanalysis is a new technology of the time – scale analysisand multi-resolution analysis, its basic idea is partlyfrequency separation to signal, that is multi-resolution

decomposition. The image signal is two-dimensional signal,wavelet transform for image analysis is image multi-resolution decomposition, the image is decomposed into adifferent space, different frequency sub-image. Throughwavelet transform, image is split into horizontal, vertical,diagonal, and low frequency four bands. Low frequencypart is called the approximation sub-image; the remainingthree parts are called the detail sub-image. 2 level waveletdecomposition process of the image shown in figure 5, HL,LH, HH are the horizontal high frequency, the vertical highfrequency and the diagonal high frequency part, LL is theapproximation low frequency part [20].

Figure5. Two level image decomposition diagram.

Wavelet image data generated by the image after wavelettransform equals to the total amount of the original imagedata; wavelet image has different characteristics with theoriginal image. The low frequency part concentrates mostof the energy of the image and represents an importantcomponent in the feel; it can also continue to carry out thedecomposition. The energy of the high-frequency part isless, which respectively represent horizontal, vertical anddiagonal part of the detailed information of the originalimage, such as the edge, texture, etc. In order both to hidethe embedded watermarking, and to achieve therequirements of robustness, the watermarking should not beembedded in the high frequency part and the low frequencypart of the image. In order to satisfy the requirements of theabove requirements, the watermarking is embedded into theintermediate frequency parts inspired by the literature [66],namely, watermarking is embedded in the second sub-band.In order to improve the robustness of the watermarking inthe embedding process, the spread spectrum principle isintroduced in [20]. Pseudo random sequence of Normaldistribution N(0,1) is used as a watermarking signal. Eachrandom sequence is of length 256. The 250th group randomsequence is picked to embed the image, and used 6 times inimage watermarking adjacent position, repeatedly. Theoriginal images are decomposed with 3 level wavelettransform, the each detail variance of 2 level sub-bands iscalculated, and the smallest region is chosen to embedwatermarking. This algorithm [20] can realize the blindwatermarking extraction and detection, and has a goodrobustness to random noise attack, cutting, noise pollutionand JPEG compression.Semi-fragile watermarks are used to detect unauthorizedchanges to an image, whereas tolerating allowed changessuch as compression. Most semi-fragile algorithms thattolerate compression assume that because compression onlyremoves the less visually significant data from an image,tampering with any data that would normally be removedby compression cannot affect a meaningful change to theimage. Scalable compression allows a single compressed


image to produce a variety of reduced resolution or reducedquality images, termed sub-images, to suit the differentdisplay or bandwidth requirements of each user. However,highly scaled sub-images remove a substantial fraction ofthe data in the original image, so the assumption used bymost semi-fragile algorithms breaks down, as tamperingwith this data allows meaningful changes to the imagecontent. The authors of [67] propose a scalable fragilewatermarking algorithm for authentication of scalableJPEG2000 compressed images. It tolerates the loss of largeamounts of image data because of resolution or qualityscaling, producing no false alarms. Yet, it also protects thatdata from tampering, detecting even minor manipulationsother than scaling, and is secure against mark transfer andcollage attacks. Experimental results demonstrate this forscaling down to 1/1024th the area of the original or to1/100th the file size.

III. INSERTING SECURITY FOR SPECIFIC APPLICATIONSThe field of medical science is now frequently usingwatermarking techniques for transferring digital images forremote diagnosis or tests. The images transferred formilitary purpose also needs to be highly secured by variousattacks. For this purpose, error correction codes can beinserted into the digital watermarked image for enhancingthe security of the image. The authors of [68] proposed atechnique of encrypting the text data before interleavingwith images to ensure greater security. Encrypting thewatermark payload and then inserting it into the image doesnot solve the purpose. To ensure security againstmodification attacks, error correction needs to be present.[69] Proposed to insert Reed Solomon (RS) codes in thewatermarked image in to correct the errors generated due tonoisy channel. The wireless channels have fluctuatingchannel characteristics and high bit-error rates. Duringimage transmission over wireless channels, the lost or errantdata is to be recovered from the received data. A remedy forthis problem is the Shannon’s well-known joint source-channel coding. The turbo channel coding provides thenear– Shannon capacity error correcting performance, wheniterative soft decoding is employed. A robust approach fortransmission of watermarked medical images is proposedby [70]. In this approach, the text data of the patientinformation is first encrypted using the encryptionalgorithm to enhance the security and then RS and LDPCconcatenation coding is applied on it for robustness. Theencrypted and RSLDPC coded text data is then embeddedinto the lower order bits(LSBs) of the medical image pixelsas a watermark using spatial domain technique. Further, thewatermarked medical image is turbo coded for its robusttransmission over impulsive noisy wireless channels. [71]Proposed an effective method to improve the robustness ofthe watermark using the ECC technique which is blockbased error correction codes with the convolution codes.Demonstrates how channel coding can improve therobustness of spatial image watermarks against JPEG DCT-based compression. Two error-correction coding (ECC)schemes are used here. One scheme, referred to as thevertical ECC (VECC), is to encode information bits in pixellevels by error-correction coding where the Gray code isused to improve the performance. The other scheme,referred to as the horizontal ECC (HECC), is to encodeinformation bits in an image plane by error-correction

coding. VECC is also used to encode the code bits of HECCin pixels. Simple single-error-correcting block codes areused in VECC and HECC. Several experiments of theseschemes were conducted on test images. The resultdemonstrates that the error correcting performance ofHECC depends on that of VECC, and accordingly, HECCenhances the capability of VECC. Consequently, HECCwith appropriate codes can achieve stronger robustness toJPEG-caused distortions than non-channel codingwatermarking schemes. The error correcting codes can alsobe applied to color image watermarking. a color imagewatermarking scheme based on the Spatio-ChromaticFourier Transform (SCFT) with direct-sequence spreadingenhanced by low density parity check (LDPC) errorcorrecting codes. The efficiency and data hiding capacity ofthe proposed watermark scheme are shown to be greatlyenhanced by the use of semi-random LDPC codes.

IV. HARDWARE IMPLEMENTATIONFor real time application of watermarking, some constraintsneed to be satisfied. First of all, the speed of processingshould be fast enough so that there may not occur chokingin data flow. Secondly, a platform for realizing suchalgorithm is needed. The platform is totally applicationdependent. For high speed applications, a processor isneeded which is capable of parallel processing. MAC(Multiply Accumulate and Carry) is the very basicoperation in signal processing. MAC units need to bepresent for faster processing. Lastly, the arithmeticoperations need to be integer type, preferably. Suchpreference is added in order to ensure faster and lesscomplex mathematical operations. The implementation ofwatermarking could be on many platforms such as software,hardware, embedded controller, DSP, etc. Systemperformance is a major parameter while designing complexsystems. The standard DSP which has Von Neumann styleof fetch operate- write back computation fails to exploit theinherent parallelism in the algorithm. For example, a 30 tapFIR filter implemented on a DSP microprocessor wouldrequire 30 MAC (Multiply Accumulate) cycles foradvancing one unit of real-time. Further, each MACoperation may consist of more than one cycle as it involvesa memory fetch, the multiply accumulate operation, and thememory write back. In contrast, a hardware implementationcan store the data in registers and perform the 30 MACoperations in parallel over a single cycle. Thus, highthroughput requirements of real-time digital systems oftendictate hardware intensive solutions [4].FPGAs provide a rapid prototyping platform. They can bereprogrammed to achieve different functionalities withoutincurring the non-recurring engineering costs typicallyassociated with custom IC fabrication. For commercialapplications like movie production, video recording, realon-spot video surveillance, where a real-time response isalways required, so a software solution is not recommendeddue to its long time delay. Since the goal of this research isa high performance encoding watermarking unit in anintegrated circuit (IC) for commercial applications, andsince FPGAs (field programmable gate arrays) haveadvantages in both fast processing speed and fieldprogrammability, it was determined that an FPGA is thebest approach to build a fast prototyping module forverifying design concepts and performance.


Several software implementations of the watermarkingalgorithms are available, but very few attempts have beenmade for hardware implementations. Softwareimplementation of watermarking has been implementedbecause of their ease of use and flexibility. Mostly softwarebased watermarking works on offline where images arecaptured through camera and stored on computer and thesoftware for watermarking runs and embeds the watermarkand then the images are distributed. This approach has thedrawback of certain amount of delay, once images arecaptured and then watermark is embedded. If attackerswould attacks the image before the watermark embeddedthen it creates issues for ownership of the originator. Sothere is a need of real-time watermarking where watermarkembedding unit reside inside the device (as digital camera)and embedding done directly when image is captured. Thehardware implementation of watermarking has advantagesin terms of reliability and high performance for area, powerand speed. This is very much crucial in some applicationslike real-time broad casting, video authentication and securecamera system for courtroom evidence. The hardwareimplementation can have advantage of parallel processing.Since watermarking process deals with processing ofwatermark and pre-processing of original content beforeembedding watermark. These two processes areindependent and can work in parallel to achieve parallelismto achieve high speed for real-time application.The researchers implemented a DWT based watermarkingalgorithm on FPGA. Lifting based DWT is better thantraditional convolution scheme for hardwareimplementation. It requires less operation than theconvolution based approach. Moreover, it allows computingan integer wavelet transform (IWT), to design lossless andlossy image encoders. It uses fewer resources, pipelinedstages for higher operating speed and lower powerconsumption on FPGA implementation. Daubechies 9/7 andLeGall 5/3 wavelets can be used for Lifting based wavelettransform. But LeGall 5/3 is proven more hardwareefficient due to its simplicity and lossless implementation.The odd and even samples values of 5/3 LeGall basedLifting scheme can be implemented by (13) and (14).(2 + 1) = (2 + 1)+ (2 ) + (2 + 2)2 (13)(2 )= (2 )+ (2 − 1) + (2 + 1) + 24 14)Where y (n) is sampled output sequence and x (n) is inputpixel. The odd samples values of Eq. (13) shows predictphase and even samples values of Eq. (14) shows updatephase. The architecture of predict & update phase areshown figure 12 and figure 13 respectively.

V. CONCLUSION

Watermarks are needed for protecting an image from beingtampered. If the images are being transmitted over a noisychannel then watermarking algorithm robust to variousnoise attacks is required. Robust watermarking algorithm isalso capable of localizing the tampered location. Keeping inmind the bandwidth considerations over Internet, JPEGcompression is very useful now days. Compressing an

image includes deleting redundancies which may beconsidered as an attack. Thus, the design of the algorithmneeds to be such that it is robust to JPEG compression andmust be capable of extracting watermark payload from theJPEG compressed image. Robust watermarking schemesare used for proving ownership claims. While, on the otherhand, fragile algorithm is used for authentication of sender.Spatial domain watermarking methods are lesscomputationally complex, thus, suitable for real timeapplications of watermarking. But these methods have lessembedding capacity. For this purpose, frequency domainmethods based on DCT, DWT or SVD are considered forwatermark embedding. The computational complexities ofsuch algorithms are decreased by using Integer transforms.Integer DCT or DWT does not involve floating pointmultiplications. Its core transformation involves onlyadditions and shifts. Tamper localization is needed to showthe regions where noise has attacked. The hardwareapproach to watermarking algorithm enhances speed ofwatermarking, avoids offline attacks and suitable for realtime applications.

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