IMAGE WATERMARKING AND ITS HARDWARE REALIZATION: A SURVEY

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1 IJEEE, Volume, Issue 4 (August, 015) e-issn: p-issn: IMAGE WATERMARKING AND ITS HARDWARE REALIZATION: A SURVEY 1 Gaurav Gupta, Kanika Sharma 1 M.E Scholar, National Institute of Technical Teachers Training & Research, Chandigarh, India Assistant Professor, National Institute of Technical Teachers Training & Research, Chandigarh, India Abstract With the increase in the use of technology in the multimedia, the threat of piracy, tampering, customer identification, forgery of digital rights and many more such problems have increased. For dealing with such situations the concept of watermarking is employed. A digital watermark is a digital signal or pattern inserted into a digital document (text, graphics and multimedia presentations). Watermarking is one of the promising solutions for tamper detection and protection of digital content. To enhance the robustness of the embedded information the patient information is coded by ECC (error correcting codes) like RS (Reed Solomon), LDPC (Low Density Parity Check) and Convolution Codes. Software based watermarking schemes are more prone to offline attacks due to the delay between image captured and embedding the watermark. Hardware based watermarking provides real time embedding process where watermark is embedded at the same time when image is captured. The goal of hardware implementation is to achieve low-power, high performance, real-time, reliable and secure watermarking system. Hardware can be realized using FPGA (Field Programmable Gate Array), DSP(Digital Signal Processors) or custom VLSI architecture. This paper surveys the various digital watermarking algorithms for real time applications involving still images. Index Terms Image Watermarking, Spatial Watermarking, Frequency Domain Watermarking, Invisible Watermarking, FPGA and Real time Watermarking. I. INTRODUCTION Digital watermarking is the act of hiding information in multimedia data (video, audio or images), for the purposes of content protection or authentication [1]. In digital image watermarking, the secret information (usually in the form of a bit stream), the watermark, is embedded into an image (cover image), in such a way, that distortion of the cover image because of watermarking is almost perceptually negligible. There are several characteristics of effective watermarks. For one, they must be difficult or impossible to remove. For another, they must survive common document modifications and transformations such as cropping and compressing image files. They must also, in principle at least, be easily detectable and removable by authorized users with such privileges (law enforcement agencies). Invisible watermarks should also be imperceptible, while visible watermarks should be perceptible enough to discourage theft but not perceptible enough to decrease the utility or appreciation of the document []. Studies in the fields of data hiding are divided to some scopes. Figure 1 show these scopes in brief [3]. Data hiding field generally includes both steganography and watermarking. They have different types, too. Figure 1 includes major types of them. Main categories are steganography and watermarking. The word steganography is derived from the Greek words stegos meaning cover and grafia meaning writing defining it as covered writing. In fact, steganography is the art and technique of hiding a message in a carrier media [5]. Main difference of steganography and watermarking is the purpose of them. Steganography tries to hide the existence of message in carrier, while watermarking tries too add a message that can be seen in a secret way. Figure 1. Different methods of data hiding. A. Need for Watermarking The art of secretly hiding and communicating information has gained immense importance in the last two decades due to the advances in generation, storage, and communication technology of digital content. Watermarking is one of the promising solutions for tamper detection and protection of digital content. Digital watermarking attempts to copyright the digital data that is freely available on the World Wide Web to protect the owner s rights. As opposed to traditional, printed watermarks, digital watermarks are transparent signatures. They are integrated within digital files as noise, or random information that already exists in the file. Thus, the detection and removal of the watermark becomes more difficult. Typically, watermarks are dispersed throughout the entire digital file such that the manipulation of one portion of the file does not alter the underlying watermark. To provide copy protection and copyright protection for digital image and video data, two complementary techniques are being developed known as Encryption and Watermarking. One more method for data hiding is which is closely correlated with watermarking known as Steganography. Steganography was basically a way of transmitting hidden (secre t) messages between allies. There are various data hiding techniques are available for security [4], [5]. International Journal of Electrical & Electronics Engineering 0

2 B. Characteristics of Watermarking There are the following important characteristics of Watermarking [4]: (i) Transparency: The most fundamental requirement for any Watermarking method shall be such that it is transparent to the end user.. (ii) Security: Watermarked information shall only be accessible to only authorized parties. They only have the right to alter the Watermark content. Encryption can be used to prevent unauthorized access of the watermarked data. (iii) Ease of embedding and retrieval: Ideally, Watermarking on digital media should be possible to be performed on the fly. The computation needed for the selected algorithm should be least. (iv) Robustness: Watermarking must be robust enough to withstand all kinds for signal processing operations attacks or unauthorized access. Any attempt, whether intentionally or unintentionally, that has a potential to alter the data content is considered as an attack. (v) Effect on bandwidth: Watermarking should be done in such a way that it does not increase the bandwidth required for transmission. If Watermarking becomes a burden for the available bandwidth, the method fails. (vi) Interoperability: Digitally watermarked content shall still be interoperable so that it can be seamlessly accessed through heterogeneous networks and can be played on various plays out devices that may be aware or unaware of watermarking techniques. C. Types of Watermarking Digital watermarking techniques can be divided into following categories: Spatial Domain Watermarking: Several different methods enable watermarking in the spatial domain. The simplest (too simple for many applications) is just to flip the lowest-order bit of chosen pixels. This approach is used commercially for journalists to inspect digital pictures from a photo-stock house before buying unmarked versions [3]. This method of spatial domain interleaving is susceptible to noise. Frequency Domain Watermarking: The image is first transformed to the frequency domain and then the low frequency components are modified to contain the text or signal. [18]. Since watermarks applied to the frequency domain will be dispersed over the entirety of the spatial image upon inverse transformation, this method is not as susceptible to defeat by cropping as the spatial technique. However, there is more a tradeoff here between invisibility and decodability, since the watermark is in effect applied indiscriminately across the spatial image [3,6]. Many authors have proposed the protecting the ownership rights through the watermarking [7, 8, 9, 10]. And also authors have implemented adaptive watermarking in the DCT domain [11-15]. Many authors have implemented the Wavelet based watermarking techniques in the Wavelet domain [16-]. Reversible Watermarking: In reversible watermarking strategy the image restored after the watermark extraction, is identical to the original cover image, pixel by pixel and bit by bit. Reversible watermarking finds widespread use in military and medical imagery, where distortion-free recovery of the original image after watermark extraction is of utmost importance [5,4&5]. However in many cases, in spite of using a reversible watermarking technique, bitby-bit recovery of the cover image may be infeasible. For example, military communication often takes place over highly noisy channels (e.g. over a temporary, low - bandwidth radio data-link setup in the battlefield or near enemy territory). Research indicates that packet error rates (PERs) of such channels, can be as high as 30%. In such scenarios, it might not be possible to correct all errors at the receiver end, in spite of using error-correcting codes. Consequently, because of the residual error in the received image, both the recovered cover image and the watermark would exhibit distortions after watermark extraction. The main purpose of any encryption mechanism is to protect the transmitted data from any unauthorized interception, whereas reversible watermarking algorithms deal with the authentication of transmitted data [6-9]. Reversible watermarking is gaining more attention for the last few years because of its increasing applications in militarycommunication, healthcare, and law-enforcement. First reversible watermarking scheme was developed by [30]. They utilized modulo addition 56 to achieve reversibility in their watermarking technique. Since then much improvements have been done in this field. [31] Developed a reversible watermarking approach by modifying the patchwork algorithm and using modulo addition 56. [30, 31] suffers from salt & pepper noise because of the use of modulo addition 56. A reversible watermarking technique without using modulo addition 56 was then introduced by [3]. It proposed the concept of compressing the least significant bit (LSB) plane of cover image to make space for the watermark to be embedded. However, the embedding capacity of this approach was limited. To improve the embedding capacity and imperceptibility of the watermarked image, [33], then proposed another approach. A number of new techniques, extensions or improved versions of the earlier techniques, have been proposed in recent years. The improvement is primarily based upon making a good imperceptibility versus capacity tradeoff [5]. [34] discussed key requirements of the watermark and classified reversible watermarking schemes into three categories: data compression, difference expansion and histogram shifting. A single reversible watermarking scheme is discussed in each of these categories. Some major challenges faced by the researchers in this field are also outlined. There can be different ways of classifying the reversible watermarking schemes. One such classification of reversible watermarking techniques is given below [5, 4]: (i) Compression based (ii) Histogram modification based (iii) Quantization based (iv) Difference Expansion based (v) Modification of frequency domain characteristics All the above schemes will be discussed later in the paper. Irreversible Watermarking: Through suitable watermarking techniques, the protection of the data can be ensured and one can know whether the received content has been tampered with or not. However, watermarking can cause damage to the sensitive information present in the cover work, and thus at the receiving end, the exact recovery of cover work may not be possible. Such Watermarking schemes are categorized as Irreversible International Journal of Electrical & Electronics Engineering 1

3 Watermarking techniques. In some applications, such loss of information can be tolerated, like Video on Demand (VOD) applications. Visible and Invisible Watermarking: Visible Watermarks are more overt means of discouraging theft and unauthorized use both by reducing the commercial value of a document and making it obvious to the criminally inclined that the document s ownership has been definitively established. Invisible watermarks should also be imperceptible, while visible watermarks should be perceptible enough to discourage theft but not perceptible enough to decrease the utility or appreciation of the document []. Robust Watermarking: Watermarking is said to be robust when the watermark payload can be retrieved from the attacked watermarked image. The attack can be blurring, enhancement, sharpening, compression, cropping or resizing of image. Any attempt, whether intentionally or unintentionally, that has a potential to alter the data content is considered as an attack. Robustness against attack is a key requirement for Watermarking and the success of this technology for copyright protection depends on its stability against attacks. A robust watermarking algorithm should be able to extract a good quality of watermark from the watermarked image even after undergoing different common image processing operations. Robust watermarking schemes are used for proving ownership claims. Fragile and Semi-Fragile Watermarking: Fragile watermarks are designed to be damaged by any changes to an image and changes to the image can be detected and located. Semi-fragile watermarks are designed to be damaged by tampering but also to remain unchanged by a set of allowed operations that may include image compression. Compression tolerance is often obtained by excluding all image data which are expected to be lost during compression, from the watermarking process. Although this ensures that the watermark will be correctly recovered from an un-tampered compressed image, it allows an attacker to tamper freely with and data that has not contributed to the watermark. This means that semifragile watermarks that are compression tolerant may not always be usable for image authentication. D. Applications of Watermarking Digital Watermarking finds application in various fields like military communication, Electronic Medical Records (EMR), data hiding in medical images, Video on Demand(VOD) services, video broadcasting digital rights, authentication of valid users, copyright protection of the publishers, online shopping and many more. The number of commercial download platforms is increasing and the current success of music and audio book stores shows the increasing acceptance of those business models [35].In medical images, patients details and the doctors views can be inserted into the medical images to form a comprehensive data bank. However, data hiding in medical images, due to their specific requirements impose certain constraints, which set some specific requirements. To preserve high quality, one may embed information in the region of non-interest (RONI) [36, 37, 38]. There are many more such applications of Watermarking which will be discussed along with the Watermarking schemes and the Hardware Implementation of such techniques for real time applications. II. WATERMARKING ALGORITHM Watermarking algorithms can be classified according to different basis. One of the most frequently used algorithms is reversible watermarking algorithm as it gives maximum probability of exact retrieval of the watermarked image. The applications of reversible watermarking can be found in the fields where the information content of the image is very sensitive and minor changes or distortion in the retrieved image may be very harmful. For example, the medical images are watermarked and then stored or transmitted for use in EMR (Electronic Medical Record). In such a case, the details in the ROI (Region of Interest) should not be altered at any cost. For this purpose, only lossless watermarking algorithm should be employed. To much extent, reversible watermarking is lossless. Such a situation is also prevalent for military applications. In this case also reversible watermarking is preferred. The pioneering work in the field of reversible or lossless watermarking could be found in [39, 40]. In [39], the bits to be overlaid will be compressed and added to the bit string, which will be embedded into the data block. [40] Reconstruct the payload from an embedded image, and then subtract the payload from the embedded image for lossless recovery of the original image. Difference Expansion: The pioneering work in this field is done by [41]. A high-capacity, high visual quality, reversible data-embedding method for digital images is presented in this work. This method can be applied to video and audio as well. [41] Calculates the differences of neighboring pixel values, and select some difference values for the difference expansion (DE). The original content restoration information, a message authentication code, and additional data (which could be any data, such as date/time information, auxiliary data, etc.) will all be embedded into the difference values. The redundancy in the image content is explored for embedding the payload in the digital image. The amount of payload added to the image accounts for the visual quality of the embedded image. The performance of data embedding algorithm can be measured by the following parameters: (a) Payload capacity limit (b) Visual quality (c) Complexity of algorithm For an 8-bits grayscale image with pixel pair (x, y), where x, y Z, 0 x, y 55, define their integer average l and difference h as = x +, h = x y (1) The inverse transform of (1) is given by (). = + h + 1, = h () The reversible integer transform pairs are known as Haar Wavelet Transform, or the S transform. The (x, y) and (l, h) International Journal of Electrical & Electronics Engineering

4 are one to one mapped. To restrict the values in the range [0, 55], the following conditions must be met: 0 + h and 0 h 55 (3) Then expansion of h takes place to embed the watermark bit b. h = h + (4) To produce watermarked pixel(, ), a reverse integer transformation is performed on h and l as shown by relation (5). = + h + 1 and = h (5) During watermark extraction, at the receiver side, again the difference between two adjacent (watermarked) pixels is computed. This time the watermark bit is extracted and the difference number contracted or restored to its original form. This allows the reversibility of the cover image as well as lossless extraction of the watermark. As shown in the results of [41], the PSNR of the retrieved image under noisy channel decreases as the payload size (the amount of data embedded). Further work has been done to improve the performance of the existing algorithm. For the multilayer embedding applications the DE algorithm suffers from degradation in the visual quality (in terms of PSNR) of the embedded image after the first layer embedding due to the use of large differences. Second, the new difference image has smaller embedding capacity than its predecessor. Each layerembedding progressively decreases the correlation not only in the embedding directions but also of the neighborhood. Thus, multiple-embedding does not effectively exploit the correlation inherent in a neighborhood [4]. Third, the algorithm can not keep its behavior smoothly because each layer embedding has its own embedding capacity limit. The sudden dip in the capacity versus distortion curve for the DE methods around 0.5bpp is the effect of multiple-layer embedding [43]. For this purpose, a modified DE is proposed by [43, 44]. [43] Improve the visual quality of the embedded image by 6dB as compared to the DE algorithm [41]. The proposed RDE method [43] uses a transformation function to reduce the value of the expansion difference h for obtaining a reduced expansion differenceh. The proposed transformation function can be represented as h = h, h < h, h The reduced difference expansion embedding is defined as: h = h + (7) To successfully restore the original difference value, a binary map (or Extraction Map) is created. The size of the Extraction Map is same as that of the number of pixel pairs. It should be noted that, the Extraction Map is considered as the side information of the proposed RDE method. Therefore, the Extraction Map should be compressed and stored for later extraction of the embedded data and (6) restoration of the original image. For application of multilayer embedding, the Extraction Map used to extract the (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 an algorithm that selects expandable differences under the same selection threshold in two difference images and embeds the payload in two orthogonal embedding directions. This scheme greatly improves image quality. The algorithm performance is smooth and varies gradually with the change of payloads. To improve the performance of the system in terms of computation power, [45] proposed a method which eliminates the need of the Location map being used in [41]. The proposed method not only retains the embedding capacity but also removes both the location map and the LSBs of changeable differences which have not been expanded from the recovery information such that all of the embedding capacity can be used to embed user s message. In addition, the embedding capacity of an image can be finely tuned according to the size of required space. Integer transform: The high hiding capacity can not be achieved only by difference expansion, so the companding technique is introduced into the embedding process so as to further increase hiding capacity. The invertible integer transform exploits the correlations among four pixels in a quad. Data embedding is carried out by expanding the differences between one pixel and each of its three neighboring pixels. The transform is proposed to calculate the difference between one pixel and each of its three neighboring pixels in a quad. The companding technique is introduced so that the differences larger than or equal to the threshold can also be expanded. Accordingly, the location map can be compressed into a very short bit stream to largely increase the embedding capacity. [46] Proposed this technique. A X group of pixels in a gray scale image I is here referred as a quad. A quad is denoted by q. = 1, 1,, 3, 4 (6) 3 4 A forward integer transform T(.) is applied to every quad of X pixels as given by (7). 1 = = 1 3 = = 1 4 (7) A forward integer transform is carried out on every quad to generate three difference numbers that are expanded to embed three watermark bits. The inverse integer transform is given by (8) = = = 1 3 = = 1 4 (8) This process is composed of three parts: 1) Companding: a quantized compression function, CQ, is respectively applied to v, v3 and v4; International Journal of Electrical & Electronics Engineering 3

5 ) Classification: each quad is classified into one of three categories with the help of three outputs vq1, vq and vq3; 3) Data embedding: it is implemented based on the quad s category. For extraction, every adjacent pixels in the marked image Iw are grouped into a quad in the same way as in embedding. T (.) is applied to each quad to get its transformed one = 1, where 1,, 3, 4 is 3 4 given by (9) if q belongs to class C1 else given by (10) if q belongs to class C. = + 3 = = + 4 (9) = + 3 = = (10) The extraction process consists of two steps: (a) Location map extraction (b) Restoration Finally (. ) is applied to reconstruct the original image I. Speed and complexity of the algorithm is an important factor to be considered in implementing such algorithm in real time systems. For implementing such algorithm the data must be processed as soon as it is coming in so that delay may not harm or disrupt the processing. For this purpose the computational complexity should not be much higher which results in faster processing of the input signals. In the existing algorithms, there is a need to create a location map which must be stored and compressed with a lossless compression technique so that no information might be lost. This compressed location map is transmitted with the image itself. A reversible watermarking scheme based on reversible contrast mapping (RCM) is proposed by [47], in which the location map is not needed. The scheme does not need additional lossless data compression, and the computational complexity is extremely low for both data embedding and extraction. This important feature makes it appropriate for real time applications. However, this method can embed only one bit into a pixel pair. Thus, in a single pass embedding, its embedding rate can not exceed 0.5 bit per pixel (bpp). In [48], the work is extended and proposes a novel reversible image watermarking scheme based on a generalized integer transform. The proposed method uses a block that contains n pixels, and (n 1) bits are embedded into each suitably selected block, where n is a positive integer. Comparing with [47], this method can provide a higher bpp while giving a better peak signal to noise ratio (PSNR). In addition, there are no data compression steps in this method, which results in fast data embedding and extraction. In general, the performance of a reversible image watermarking method is evaluated in two aspects: the embedding capacity and the visual quality. More precisely, we expect to increase the embedding capacity as high as possible while keeping distortion low. Data hiding schemes with high embedding capacity have also been proposed by [49] which achieve embedding capacity as high as 1.85 bpp in Lena image. Besides, by pre-estimating the embedding distortion, one can suitably select embeddable blocks so that the visual quality of the watermarked image is well guaranteed. Furthermore, extensive experiments show that the novel method performs better than some state-of-the-art algorithms. While keeping the distortion low, [50] proposes a method to increase the embedding capacity. The integer transform is calculated based on the difference of adjacent pixels. It can be applied to a pixel block of arbitrary size with adjustable capacities. Moreover, to reduce the distortion further, the integer transform is conducted by conditionally embedding data only when the estimated distortion is acceptable. Another method to increase the embedding capacity based on D lifting Wavelet transform is proposed by [51]. This method presents the reversible watermarking algorithm which aims to increase embedding capacity by using the proposed block linking method, instead of the location map. This method which is based on integer transform is applied on the detail coefficients subbands of -D lifting wavelet transform. The main advantage of this algorithm is that the block linking method is faster and simpler compared to the location map proposed by [41] which need a complex loss less compression. This method can achieve high capacity for image watermarking while preserve good image quality. Compression based: The technique of compression is also used in the already discussed algorithms. There are many more such algorithms which employ compression for compressing some of the bit planes of an image matrix for making for embedding data. Lowest bit planes are altered so that the distortion in the image is perceptually negligible. A generalization of the well known least significant bit (LSB) modification is proposed by [5] as the data-embedding method, which introduces additional operating points on the capacity-distortion curve. This is a spatial domain technique for watermarking. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditional entropy coder which utilizes unaltered portions of the host signal as side-information improves the compression efficiency and, thus, the lossless data embedding capacity. In this method, the LSB of the image data is replaced by a payload data bit per input sample. If larger embedding capacity is required then two or more bits are replaced according to the need. During extraction, these bits are read in the same scanning order, and payload data is reconstructed. A generalization of the LSB-embedding method, namely G-LSB, is employed in [5]. If the host signal is represented by a vector, the G- LSB embedding and extraction processes can be represented as = ( ) + (11) = ( ) = ( ) (1) International Journal of Electrical & Electronics Engineering 4

6 Where represents the signal containing the embedded information, 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 compression to the remainders. In [5], arithmetic coding is used. Concatenate the compressed remainders and the watermark W to form the bit stream H. Convert H to L-ary symbols and then to the quantized pixels. This gives the watermarked image. In the embedding phase, the lowest levels of the signal samples are replaced (over -written) by the watermark payload using a quantization step followed by an addition. Extraction is carried out by L-level quantization of watermarked pixels. This time the remainders are converted from L-ary form to binary form. After watermark extraction the leftover portion of this binary bit stream is decompressed to get back the original remainders. The original cover image can be restored bitby-bit when the recovered remainders are added to the quantized pixels. Modification of Frequency Domain Characteristics: In such scheme, watermark is embedded in the spectral components of the image. Spatial domain algorithm can hide large amounts of data, but the robustness of the algorithm is Poor performance and can severely damage the watermarking, compared with the spatial domain watermarking algorithm, transform domain watermarking algorithm has high hidden, strong robustness, good compatibility [0]. Bit shifting is employed in spatial domain for watermarking the image which suffered from low capacity and overflow or underflow problems. The approach proposed by [53] is especially efficient for audio signals by employing the companding techniques typically used in telephony systems. The high capacity generated by this approach can be attributed to the highly concentrated distribution shape of the histograms of common audio signals, which looks like a Laplacian distribution shape around the zero point. This fact is especially suitable for the bit-shift operation of the companding technique because it makes the companding error small enough to save more space for the watermark bits. On the other hand, for usual audio signals, the perceptional distortion is limited to a loudness difference which is attributed to amplified sample amplitudes caused by bit-shift operations. However, natural images histogram is shaped differently. And bit-shifts on pixel values will cause much noticeable distortion to images. These two facts make this companding and bit-shift based approach hard to be suitable for reversible image watermarking applications, although the capacity performance is quite a desirable advantage of this reversible technique. This reversible watermarking approach proposed in [53] shows high capacity which is close to 1 bit per sample for audio signals. But due to the disadvantage of natural images histogram, this approach is obviously not suitable for images. A -dimensional integer DCT based approach is proposed in [54] to circumvent the problems mentioned above and use the bit-shift operation of companding technique successfully in reversible watermarking for images. [54] Choose AC coefficients in the integer DCT domain for the bit-shift operation, and therefore the capacity and the quality of the watermarked image can be adjusted by selecting different numbers of coefficients of different frequencies. To prevent overflows and underflows in the spatial domain caused by modification of the DCT coefficients, we design a block discrimination structure to find suitable blocks that can be used for embedding without overflow or underflow problems. We can also use this block discrimination structure to embed an overhead of location information of all blocks suitable for embedding. With this scheme, watermark bits can be embedded in the saved LSBs of coefficient blocks, and retrieved correctly during extraction, while the original image can be restored perfectly. First, the 51x51 image is broken down into 8x8 blocks of pixels. Integer DCT is calculated for each block and then coefficients are selected for embedding the payload data according to twice try based structure for block discrimination. Left shift the selected coefficients and insert the watermark bits into the LSB positions. The coefficients are modified now. Lastly apply inverse integer DCT to produce the final watermarked image. The embedding and extracting process for 8x8 image block is shown in figure [54]. Figure. Embedding and Extraction process for 8x8 image block [54]. In [54], the performance is compromised in high PSNR ranges (approximately > 40dB) compared with their good performance in middle and low PSNR ranges. Histogram modification schemes [55] and [56] for reversible watermarking have good performance in high PSNR ranges because of the relatively low distortion of the histogram shifting operation, However, the PSNR dynamic ranges resulting from [55] and [56] are highly restricted by original images' pixel value histograms when the histogram modifications are directly performed in the spatial domain.[57] Proposes a reversible watermarking scheme using histogram modification in the 8x8 integer DCT domain. This proposed scheme exploits the high energy concentrating property of integer DCT and allows fine coefficient selection for watermarking, and thus, shows equivalent or higher performance and wider quality (PSNR) ranges compared to [55] and [56]. The histogram modification techniques in both [55] and [56] are directly used in the spatial domain and the algorithms' performance is determined by the pixel distribution of the original image. Using histogram modification in the integer DCT domain in light of integer DCT's energy concentration property, and expect to heighten the amplitude of the peak point P and thus increase the capacity. Similar to a float-point DCT, an integer DCT has the energy concentration property, which can be used to improve the capacity of histogram International Journal of Electrical & Electronics Engineering 5

7 modification scheme. Unlike the float-point DCT, the integer DCT is lossless and suitable for reversible watermarking. The coefficients of the integer DCT (of the 8x8 pixel blocks) are calculated. Groups of coefficients are calculated having a position (p, q) in all the 8x8 image blocks. For example, coefficients having position (, 3) in every image block is grouped in G(, 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 elements in every coefficient group will be equal to the total number of image blocks. For every coefficient group, a histogram is calculated. In this way, total 64 histograms are calculated. For each coefficient group G(p, q), we embed the watermark reversibly by histogram modification on its histogram H(p, q)( 1, 8). The principle of histogram modification used in [55, 56] is used in [57] for modification of coefficient histogram H(p, q). The transform from spatial domain to integer DCT domain should improve the whole algorithm's capacity and image fidelity. A relatively simple scheme to store the overhead information is employed in [57]. Unlike twice try block discrimination scheme used [54], [57] uses a comparatively less complex scheme to store and process the overhead information. The original image can be divided into 16x16 blocks and we pick out some blocks as overhead blocks according to a secret key and then replace the LSBs of pixels in these blocks with the bits of the overhead. And the original LSBs are embedded with watermark bits into the saved space from the histogram modification technique. During extraction, we can use the secret key to find out these blocks and extract the overhead information. The proposed scheme [57] has relatively high capacity in the high PSNR range. Compared with other reversible image watermarking schemes [41, 54, 55, 56], this scheme shows equivalent or higher performance, and particularly, capability of fine adjustment of the watermarked image's quality (PSNR) by selecting different numbers of coefficients. Another approach for reversible and fragile watermarking is proposed by [58]. In this approach Discrete Cosine Transform (DCT) is used for generating the coefficients of the 8x8 blocks of image. These coefficients are then embedded with information for authentication at the receiving end. According to the characteristics of compression transformation, coefficients of high absolute values are mainly localized in low-frequency domains; however, coefficients in high-frequency domains always have relatively low absolute values. The watermark is then embedded in the high frequency contents. The problem of overflow and underflow of pixel values is also handled successfully. The results of [58] are shown in figure 3. The quality of watermarked image figure 3(b) doesn t obviously drop 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 after JPEG 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 (quality factor is 90), after that the tampered regions in compressed image are detected, as shown in figure 3(c), which is not revised. The white parts in figure 3(c) are the tampered regions after the watermarked image is compressed. Watermarks have been seriously damaged by observing the figure 3(c), which has shown that the watermarks are quite fragile even when the quality factor of JPEG-compression is still high. Then we put a clock pattern on the left-top of figure 3(b) to create a tampered image, as shown in figure 3(d). Figure 3(e) is the corresponding image of tampering detection for figure 3(d). Figure 3(f) is the result of revision for figure 3(e) by using the revising method proposed [58] (α =5 when the revision is made). Clearly, the detection of tampered regions is more accurate after the revising method is used. In medical image applications, most of the proposed reversible watermarking methods are spatial-domain watermarking schemes, such as histogram modification [59] and difference expansion based embedding scheme [60]. To improve the capacity and reduce the embedding induced distortion, most of the schemes exploiting the ROI (Region Of Interest) and RONI (Region Of Non -Interest) of the medical images. Unlike ordinary images, medical images have consecutive ROI and RONI areas, which offer benefits for watermark embedding algorithms. [61] Presents a reversible watermarking scheme based on Integer DCT and DE. The energy threshold selecting scheme introduces a new way for choosing the ROI blocks of the medical image. Instead of the rectangle area selection [6] and block based selection [63], the energy threshold is used to select the ROI blocks adaptively. The goal of using these methods is to achieve reversibility while minimizing the perceptional distortion. The Discrete Cosine Transform (DCT) has been widely used in the algorithm design of digital watermarking. As DCT is a transform of real numbers, the output of DCT transform is in real number form, which may cause some extra rounding off in image processing. Integer DCT transform uses integers instead of regular floating point numbers to fill the transformation matrix. The transformation core is an integer transform, which has no floating-point calculations and get a high accuracy. The International Journal of Electrical & Electronics Engineering 6

8 core transformation can be completed through simple addition and shift operations, which can reduce the computing complexity. Integer arithmetic avoids accuracy error, ensures the reversibility of coding, and solves the matching problem between encoders' and decoders' forward transform and inverse transform; this characteristic has been used in some watermarking schemes [54]. The scheme proposed by [61] involves dividing the image into 4x4 blocks and then calculating integer DCT coefficients of each block. Energy of each block is calculated and the block having energy greater than specified threshold energy is selected for embedding the secret bits into it. Finally apply inverse integer DCT of each block. This gives the final watermarked image. the middle frequency coefficients are used to embed the secret bits, because the DE embedding needs coefficients to carry 1 bit, the coefficient number 8 and number 9 are chosen for embedding as shown in Figure 4. Low frequency components are not used for embedding purpose because they greatly affect intensity of the image, hence disturbing them may seriously induce perceptual distortion. High frequency components may be used for embedding secret bits but it hinders the image processing operations. The above mentioned algorithms do not stand suitable for applications demanding larger embedding capacities. The embedding capacity in all the above mentioned algorithms [54, 57, 58 & 61] is found to be less in comparison to [64]. Integer wavelet transform is used for transforming the image into frequency domain in [64]. This algorithm hides data into one (or more) middle bit plane(s) of the integer wavelet transform coefficients in the middle and high frequency sub-bands.the above mentioned algorithms do not stand suitable for applications demanding larger embedding capacities. The embedding capacity in all the above mentioned algorithms [54, 57, 58 & 61] is found to be less in comparison to [64]. Integer wavelet transform is used for transforming the image into frequency domain in [64]. This algorithm hides data into one (or more) middle bit plane(s) of the integer wavelet transform coefficients in the middle and high frequency sub-bands. Figure4. 4x4 matrix of coefficients. It can embed much more data compared with the existing distortion less data hiding techniques and satisfy the imperceptibility requirement. The image histogram modification is used to prevent grayscales from possible overflowing. Spatial domain algorithm can hide large amounts of data, but the robustness of the algorithm is Poor performance 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]. Wavelet analysis is a new technology of the time scale analysis and multi-resolution analysis, its basic idea is partly frequency separation to signal, that is multi-resolution. decomposition. The image signal is two-dimensional signal, wavelet transform for image analysis is image multiresolution decomposition, the image is decomposed into a different space, different frequency sub-image. Through wavelet transform, image is split into horizontal, vertical, diagonal, and low frequency four bands. Low frequency part is called the approximation sub-image; the remaining three parts are called the detail sub-image. level wavelet decomposition process of the image shown in figure 5, HL, LH, HH are the horizontal high frequency, the vertical high frequency and the diagonal high frequency part, LL is the approximation low frequency part [0]. Figure5. Two level image decomposition diagram. Wavelet image data generated by the image after wavelet transform equals to the total amount of the original image data; wavelet image has different characteristics with the original image. The low frequency part concentrates most of the energy of the image and represents an important component in the feel; it can also continue to carry out the decomposition. The energy of the high-frequency part is less, which respectively represent horizontal, vertical and diagonal part of the detailed information of the original image, such as the edge, texture, etc. In order both to hide the embedded watermarking, and to achieve the requirements of robustness, the watermarking should not be embedded in the high frequency part and the low frequency part of the image. In order to satisfy the requirements of the above requirements, the watermarking is embedded into the intermediate 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 in the embedding process, the spread spectrum principle is introduced in [0]. Pseudo random sequence of Normal distribution N(0,1) is used as a watermarking signal. Each random sequence is of length 56. The 50 th group random sequence is picked to embed the image, and used 6 times in image watermarking adjacent position, repeatedly. The original images are decomposed with 3 level wavelet transform, the each detail variance of level sub-bands is calculated, and the smallest region is chosen to embed watermarking. This algorithm [0] can realize the blind watermarking extraction and detection, and has a good robustness to random noise attack, cutting, noise pollution and JPEG compression. Semi-fragile watermarks are used to detect unauthorized changes to an image, whereas tolerating allowed changes such as compression. Most semi-fragile algorithms that tolerate compression assume that because compression only removes the less visually significant data from an image, tampering with any data that would normally be removed by compression cannot affect a meaningful change to the image. Scalable compression allows a single compressed International Journal of Electrical & Electronics Engineering 7

9 image to produce a variety of reduced resolution or reduced quality images, termed sub-images, to suit the different display or bandwidth requirements of each user. However, highly scaled sub-images remove a substantial fraction of the data in the original image, so the assumption used by most semi-fragile algorithms breaks down, as tampering with this data allows meaningful changes to the image content. The authors of [67] propose a scalable fragile watermarking algorithm for authentication of scalable JPEG000 compressed images. It tolerates the loss of large amounts of image data because of resolution or quality scaling, producing no false alarms. Yet, it also protects that data from tampering, detecting even minor manipulations other than scaling, and is secure against mark transfer and collage attacks. Experimental results demonstrate this for scaling down to 1/104th the area of the original or to 1/100th the file size. III. INSERTING SECURITY FOR SPECIFIC APPLICATIONS The field of medical science is now frequently using watermarking techniques for transferring digital images for remote diagnosis or tests. The images transferred for military purpose also needs to be highly secured by various attacks. For this purpose, error correction codes can be inserted into the digital watermarked image for enhancing the security of the image. The authors of [68] proposed a technique of encrypting the text data before interleaving with images to ensure greater security. Encrypting the watermark payload and then inserting it into the image does not solve the purpose. To ensure security against modification attacks, error correction needs to be present. [69] Proposed to insert Reed Solomon (RS) codes in the watermarked image in to correct the errors generated due to noisy channel. The wireless channels have fluctuating channel characteristics and high bit-error rates. During image transmission over wireless channels, the lost or errant data is to be recovered from the received data. A remedy for this problem is the Shannon s well-known joint sourcechannel coding. The turbo channel coding provides the near Shannon capacity error correcting performance, when iterative soft decoding is employed. A robust approach for transmission of watermarked medical images is proposed by [70]. In this approach, the text data of the patient information is first encrypted using the encryption algorithm to enhance the security and then RS and LDPC concatenation coding is applied on it for robustness. The encrypted and RSLDPC coded text data is then embedded into the lower order bits(lsbs) of the medical image pixels as a watermark using spatial domain technique. Further, the watermarked medical image is turbo coded for its robust transmission over impulsive noisy wireless channels. [71] Proposed an effective method to improve the robustness of the watermark using the ECC technique which is block based error correction codes with the convolution codes. Demonstrates how channel coding can improve the robustness of spatial image watermarks against JPEG DCTbased compression. Two error-correction coding (ECC) schemes are used here. One scheme, referred to as the vertical ECC (VECC), is to encode information bits in pixel levels by error-correction coding where the Gray code is used to improve the performance. The other scheme, referred to as the horizontal ECC (HECC), is to encode information bits in an image plane by error-correction coding. VECC is also used to encode the code bits of HECC in pixels. Simple single-error-correcting block codes are used in VECC and HECC. Several experiments of these schemes were conducted on test images. The result demonstrates that the error correcting performance of HECC depends on that of VECC, and accordingly, HECC enhances the capability of VECC. Consequently, HECC with appropriate codes can achieve stronger robustness to JPEG-caused distortions than non-channel coding watermarking schemes. The error correcting codes can also be applied to color image watermarking. a color image watermarking scheme based on the Spatio-Chromatic Fourier Transform (SCFT) with direct -sequence spreading enhanced by low density parity check (LDPC) error correcting codes. The efficiency and data hiding capacity of the proposed watermark scheme are shown to be greatly enhanced by the use of semi-random LDPC codes. IV. HARDWARE IMPLEMENTATION For real time application of watermarking, some constraints need to be satisfied. First of all, the speed of processing should be fast enough so that there may not occur choking in data flow. Secondly, a platform for realizing such algorithm is needed. The platform is totally application dependent. For high speed applications, a processor is needed which is capable of parallel processing. MAC (Multiply Accumulate and Carry) is the very basic operation in signal processing. MAC units need to be present for faster processing. Lastly, the arithmetic operations need to be integer type, preferably. Such preference is added in order to ensure faster and less complex mathematical operations. The implementation of watermarking could be on many platforms such as software, hardware, embedded controller, DSP, etc. System performance is a major parameter while designing complex systems. The standard DSP which has Von Neumann style of fetch operate- write back computation fails to exploit the inherent parallelism in the algorithm. For example, a 30 tap FIR filter implemented on a DSP microprocessor would require 30 MAC (Multiply Accumulate) cycles for advancing one unit of real-time. Further, each MAC operation may consist of more than one cycle as it involves a memory fetch, the multiply accumulate operation, and the memory write back. In contrast, a hardware implementation can store the data in registers and perform the 30 MAC operations in parallel over a single cycle. Thus, high throughput requirements of real-time digital systems often dictate hardware intensive solutions [4]. FPGAs provide a rapid prototyping platform. They can be reprogrammed to achieve different functionalities without incurring the non-recurring engineering costs typically associated with custom IC fabrication. For commercial applications like movie production, video recording, real on-spot video surveillance, where a real-time response is always required, so a software solution is not recommended due to its long time delay. Since the goal of this research is a high performance encoding watermarking unit in an integrated circuit (IC) for commercial applications, and since FPGAs (field programmable gate arrays) have advantages in both fast processing speed and field programmability, it was determined that an FPGA is the best approach to build a fast prototyping module for verifying design concepts and performance. International Journal of Electrical & Electronics Engineering 8