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

**IMAGE WATERMARKING AND ITS**

**HARDWARE REALIZATION: A SURVEY**

**1**

**Gaurav Gupta,**

**2**

**Kanika Sharma**

1_{M.E Scholar, National Institute of Technical Teachers Training & Research, Chandigarh, India}
2

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 [2]. 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*

*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 [16-22].

**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 [5,24&25]. However in many cases,

in spite of using a reversible watermarking technique, bit-by-bit recovery of the cover image may be infeasible. 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 [26-29]. 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 256 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 256. [30, 31] suffers from salt & pepper noise because of the use of modulo addition 256. A reversible watermarking technique without using modulo addition 256 was then introduced by [32]. 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, 24]:

(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.

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 [2].

**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
semi-fragile 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≤*255, define their integer average l and*
*difference h as*

= x +_{2} , ℎ

= x − y (1)

The inverse transform of (1) is given by (2).

= + ℎ + 1_{2} ,

= − ℎ_{2} (2)

are one to one mapped. To restrict the values in the range [0, 255], the following conditions must be met:

0 ≤ + ℎ + 1_{2} ≤ 255 and 0 ≤ − ℎ_{2}

≤ 255 (3)

*Then expansion of h takes place to embed the watermark bit*
b.

ℎ*′*
= 2ℎ

+ (4)

To produce watermarked pixel( *′*_{,} *′*_{)} _{, a reverse integer}

transformation is performed on ℎ*′* _{and l as shown by}

relation (5).

*′*_{= +} ℎ

*′*_{+ 1}

2 and *′* = −

ℎ*′*

2 (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
layer-embedding 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 [42]. 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| − 2}⌊ | |⌋|ℎ|,_{,} |ℎ| < 2_{|ℎ| ≥ 2} (6)

The reduced difference expansion embedding is defined as:

ℎ*′*
= 2ℎ

+ (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

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 2X2 group of pixels in a gray scale image I is here referred as a quad. A quad is denoted by q.

= 1_{3} 2_{4 ,} 1, 2, 3, 4 (6)

A forward integer transform T(.) is applied to every quad of 2X2 pixels as given by (7).

1 = 1 + 2 + 3 + 4_{4}

2 = 1 − 2 3 = 1 − 3

4 = 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 2

3 4 = 13 24

1 = 1 + 2 + 3 + 4

4 2 = 1 − 2 3 = 1 − 3

4 = 1 − 4 (8)

This process is composed of three parts:

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

*For extraction, every 2×2 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 2

3 4 , where 1, 2, 3, 4 is

given by (9) if q belongs to class C1 else given by (10) if q belongs to class C2.

2 = 2 + 2

3 = 2 + 3

4 = 2 + 4 (9)

2 = 2 _{2 + 2}2

3 = 2 _{2 + 3}3

4 = 2 _{2 + 4}4 (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 2D 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 sub-bands of 2-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 [52] 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
[52]. If the host signal is represented by a vector, the
G-LSB embedding and extraction processes can be
represented as

= ( ) + (11)

= − ( )

=

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 [52], 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 bit-by-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 [20]. 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 2-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 512x512 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 2 [54].

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

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 (2, 3) in every 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 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.

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 2 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 multi-resolution 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. 2 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 [20].

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 [20]. Pseudo random sequence of Normal distribution N(0,1) is used as a watermarking signal. Each random sequence is of length 256. The 250thgroup 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 2 level sub-bands is calculated, and the smallest region is chosen to embed watermarking. This algorithm [20] can realize the blind watermarking extraction and detection, and has a good robustness to random noise attack, cutting, noise pollution and JPEG compression.

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 JPEG2000 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 1/1024th 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 source-channel coding. The turbo source-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 DCT-based 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].

Several software implementations of the watermarking algorithms are available, but very few attempts have been made for hardware implementations. Software implementation of watermarking has been implemented because of their ease of use and flexibility. Mostly software based watermarking works on offline where images are captured through camera and stored on computer and the software for watermarking runs and embeds the watermark and then the images are distributed. This approach has the drawback of certain amount of delay, once images are captured and then watermark is embedded. If attackers would attacks the image before the watermark embedded then it creates issues for ownership of the originator. So there is a need of real-time watermarking where watermark embedding unit reside inside the device (as digital camera) and embedding done directly when image is captured. The hardware implementation of watermarking has advantages in terms of reliability and high performance for area, power and speed. This is very much crucial in some applications like real-time broad casting, video authentication and secure camera system for courtroom evidence. The hardware implementation can have advantage of parallel processing. Since watermarking process deals with processing of watermark and pre-processing of original content before embedding watermark. These two processes are independent and can work in parallel to achieve parallelism to achieve high speed for real-time application.

The researchers implemented a DWT based watermarking algorithm on FPGA. Lifting based DWT is better than traditional convolution scheme for hardware implementation. It requires less operation than the convolution based approach. Moreover, it allows computing an integer wavelet transform (IWT), to design lossless and lossy image encoders. It uses fewer resources, pipelined stages for higher operating speed and lower power consumption on FPGA implementation. Daubechies 9/7 and LeGall 5/3 wavelets can be used for Lifting based wavelet transform. But LeGall 5/3 is proven more hardware efficient due to its simplicity and lossless implementation. The odd and even samples values of 5/3 LeGall based Lifting scheme can be implemented by (13) and (14).

(2 + 1) = (2 + 1)

+ (2 ) + (2 + 2)_{2} (13)

(2 ) = (2 )

+ (2 − 1) + (2 + 1) + 2_{4} 14)

Where y (n) is sampled output sequence and x (n) is input pixel. The odd samples values of Eq. (13) shows predict phase and even samples values of Eq. (14) shows update phase. The architecture of predict & update phase are shown figure 12 and figure 13 respectively.

**V. CONCLUSION**

Watermarks are needed for protecting an image from being tampered. If the images are being transmitted over a noisy channel then watermarking algorithm robust to various noise attacks is required. Robust watermarking algorithm is also capable of localizing the tampered location. Keeping in mind the bandwidth considerations over Internet, JPEG compression is very useful now days. Compressing an

image includes deleting redundancies which may be considered as an attack. Thus, the design of the algorithm needs to be such that it is robust to JPEG compression and must be capable of extracting watermark payload from the JPEG compressed image. Robust watermarking schemes are used for proving ownership claims. While, on the other hand, fragile algorithm is used for authentication of sender. Spatial domain watermarking methods are less computationally complex, thus, suitable for real time applications of watermarking. But these methods have less embedding capacity. For this purpose, frequency domain methods based on DCT, DWT or SVD are considered for watermark embedding. The computational complexities of such algorithms are decreased by using Integer transforms. Integer DCT or DWT does not involve floating point multiplications. Its core transformation involves only additions and shifts. Tamper localization is needed to show the regions where noise has attacked. The hardware approach to watermarking algorithm enhances speed of watermarking, avoids offline attacks and suitable for real time applications.

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