Claims:1. A system (100) for embedding digital information to secure an image, the system comprising:
an information input unit (104) adapted to receive an image;
an encoder (110) operatively coupled to the information input unit, the encoder adapted to encode first bits of digital information and second bits of digital information onto the image;
a decoder (116) coupled to a receiver, the decoder adapted to decode the received encoded information; and
a processor (106) coupled with a memory (108), said memory storing instructions executable by the processor to:
receive, from the information input unit, the image;
segment, the received image into a plurality of blocks to select a high entropy block from the plurality of blocks;
apply, Discrete Wavelet Transform (DWT) on each of the selected high entropy blocks, to determine a plurality of sub-bands of the image;
select, first sub-band and second sub-band from the plurality of sub-bands to calculate mean and variance for each of the first sub-band and second sub-band;
determine, adjustable strength factor, for each of the first sub-band and second sub-band,
wherein, upon determining the adjustable strength factor of the corresponding sub-bands, the processor configured to encode, at the encoder (110), the first bits of digital information onto the first sub-band and second bits of digital information onto the second sub-band to ensure protection for the image,
wherein, the modified image is obtained by combining the segmented plurality of blocks using the inverse DWT; and
receive, at the receiver (114), the modified image, wherein, the modified image is decoded at the decoder (116) to extract the encoded digital information from the corresponding selected sub-bands of the image.
2. The system as claimed in claim 1, wherein the decoder configured to extract the modified image by segmenting the modified image into plurality of blocks to select the high entropy block from the plurality of blocks.
3. The system as claimed in claim 1, wherein the DWT is applied to each of the selected high entropy blocks of the received modified image to determine the plurality of sub-bands of the image.
4. The system as claimed in claim 1, wherein the first sub-band and second sub-band are selected from the plurality of sub-bands of the received modified image to calculate mean and variance using Gaussian distribution.
5. The system as claimed in claim 1, wherein the decoder is configured to extract the first bits of digital information from the first sub-band, and extract the second bits of digital information from the second sub-band to obtain recovered image.
6. The system as claimed in claim 1, wherein the plurality of sub-bands is low-low (LL) sub-band, high-low (HL)sub-band, low-high (LH)sub-band and high-high (HH)sub-band.
7. The system as claimed in claim 1, wherein the first sub-band is LL sub-band
8. The system as claimed in claim 1, wherein the second sub-band is LH sub-band.
9. A method (200) for embedding digital information to secure an image, the method comprising:
obtaining (202), at an information input unit, the image, the information input unit;
receiving (204), at a computing device, from the information input unit, the image;
segmenting (206), at the computing device, the received image into a plurality of blocks to select a high entropy block from the plurality of blocks;
applying (208), at the computing device, Discrete Wavelet Transform (DWT) on each of the selected high entropy blocks, to determine a plurality of sub-bands of the image; and
selecting (210), at the computing device, first sub-band and second sub-band from the plurality of sub-bands to calculate mean and variance for each of the first sub-band and second sub-band;
determining (212), at the computing device, adjustable strength factor, for each of the first sub-band and second sub-band;
wherein, upon determining the adjustable strength factor of the corresponding selected sub-bands, the computing device configured to encode, at an encoder operatively coupled to the information input unit, the first bits of digital information onto the first sub-band and second bits of digital information onto the second sub-band to ensure protection for the image,
wherein, the modified image is obtained by combining the segmented plurality of blocks using the inverse DWT; and
receiving (214), at a receiver, the modified image, wherein, the modified image is decoded, at a decoder coupled to the receiver, to extract the encoded digital information from the corresponding selected sub-bands of the image.

Description:TECHNICAL FIELD
[0001] The present disclosure relates, in general, to digital image watermarking, and more specifically, relates to a system and method for embedding digital information using kurtosis to secure a media object.

BACKGROUND
[0002] Generally, image watermarking algorithms use a single watermark for copyright protection. A digital watermark is a confidential signal which can be hidden directly in a digital media such as, image, audio, video, and the like. It is generally invisible and inseparable from the multimedia object and is used to identify the ownership and copyright of the same. Desirably, a watermark must be robust, imperceptible and intolerant against the changes in the carrier signal under various intentional and unintentional attacks. Most of the methods known in the art, refer to the embedding of a single watermark, however, they result in significant drawbacks.
[0003] Although multiple watermarking techniques exist in the art for natural images, these techniques, however, has limited solutions for medical image watermarking, also these existing techniques failed to achieve better image quality assessment (IQA) parameters of watermarked images, with less security and robustness
[0004] Therefore, there is a need in the art to provide a means to improve the quality, security and enhance the robustness of the watermark by solving the aforementioned problems.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] An object of the present disclosure relates, in general, to digital image watermarking, and more specifically, relates to a system and method for embedding digital information to secure a media object.
[0006] Another object of the present disclosure is to provide a system that can maintain the invisibility and quality of the watermarked images, the process of watermark embedding and its recovery is based on statistical modelling which helps to extract watermarks with better performance.
[0007] Another object of the present disclosure is to provide a system that can evaluate the strength of the watermark.
[0008] Another object of the present disclosure is to provide a system that can perform efficiently in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC) of watermarked images and Bit Error Rate (BER) of recovered watermark(s)in comparison to the existing techniques.
[0009] Another object of the present disclosure is to provide a system in which the utmost authenticity is maintained by watermarking the patient medical image with limited capacity and its related text in the single image. This helps to reduce the chances of wrong diagnosis which otherwise can even cost a life.
[0010] Another object of the present disclosure is to provide a system that can improve the balance of robustness, capacity and imperceptibility by the quality assessment parameters such as PSNR, NCC, Structural Similarity Index Measure(SSIM)and Gradient Mean Structural Deviation (GMSD).
[0011] Another object of the present disclosure is to provide a system that can maintain the confidentiality of the patient details and can reduce the cases of medical thefts.
[0012] Yet another object of the present disclosure is to provide a system that can overcome various attacks in healthcare sector and the like

SUMMARY
[0013] The present disclosure relates, in general, to digital image watermarking, and more specifically, relates to a system and method for embedding digital information using kurtosis to secure a media object.
[0014] In an aspect, the present disclosure provides a system for embedding digital information to secure an image, the system including an information input unit adapted to receive an image, an encoder operatively coupled to the information input unit, the encoder adapted to encode first bits of digital information and second bits of digital information onto the image, a decoder coupled to a receiver, the decoder adapted to decode the received encoded information, a processor coupled with a memory, said memory storing instructions executable by the processor to receive, from the information input unit, the image; segment, the received image into a plurality of blocks to select a high entropy block from the plurality of blocks, apply, Discrete Wavelet Transform (DWT) on each of the selected high entropy blocks, to determine a plurality of sub-bands of the image, and select, first sub-band and second sub-band from the plurality of sub-bands to calculate mean and variance for each of the first sub-band and second sub-band, and determine, adjustable strength factor, for each of the first sub-band and second sub-band, wherein, upon determining the adjustable strength factor of the corresponding sub-bands, the processor configured to encode, at the encoder, the first bits of digital information onto the first sub-band and second bits of digital information onto the second sub-band to ensure protection for the image, wherein, the modified image is obtained by combining the segmented plurality of blocks using the inverse DWT, and receive, at the receiver, the modified image, wherein, the modified image is decoded at the decoder to extract the encoded digital information from the corresponding selected sub-bands of the image.
[0015] In an embodiment, the decoder configured to extract the modified image by segmenting the modified image into plurality of blocks to select the high entropy block from the plurality of blocks.
[0016] In another embodiment, the DWT can be applied to each of the selected high entropy blocks of the received modified image to determine the plurality of sub-bands of the image.
[0017] In another embodiment, the first sub-band and second sub-band can be selected from the plurality of sub-bands of the received modified image to calculate mean and variance using Gaussian distribution.
[0018] In another embodiment, the decoder can be configured to extract the first bits of digital information from the first sub-band, and extract the second bits of digital information from the second sub-band to obtain recovered image.
[0019] In another embodiment, the plurality of sub-bands can be low-low (LL) sub-band, high-low (HL) sub-band, low-high (LH) sub-band and high-high (HH) sub-band.
[0020] In another embodiment, the first sub-band can be LL sub-band
[0021] In another embodiment, the second sub-band can be LH sub-band.
[0022] In an aspect, the present disclosure provides a method for embedding digital information to secure an image, the method including obtaining, at an information input unit, the image, the information input unit, receiving, at a computing device, from the information input unit, the image, segmenting, at the computing device, the received image into a plurality of blocks to select a high entropy block from the plurality of blocks, applying, at the computing device, Discrete Wavelet Transform (DWT) on each of the selected high entropy blocks, to determine a plurality of sub-bands of the image, and selecting, at the computing device, first sub-band and second sub-band from the plurality of sub-bands to calculate mean and variance for each of the first sub-band and second sub-band, and determining, at the computing device, adjustable strength factor, for each of the first sub-band and second sub-band, wherein, upon determining the adjustable strength factor of the corresponding selected sub-bands, the computing device configured to encode, at an encoder operatively coupled to the information input unit, the first bits of digital information onto the first sub-band and second bits of digital information onto the second sub-band to ensure protection for the image, wherein, the modified image is obtained by combining the segmented plurality of blocks using the inverse DWT, and receiving, at a receiver, the modified image, wherein, the modified image is decoded, at a decoder coupled to the receiver, to extract the encoded digital information from the corresponding selected sub-bands of the image.
[0023] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0024] The following drawings form part of the present specification and are included to further illustrate aspects of the present disclosure. The disclosure may be better understood by reference to the drawings in combination with the detailed description of the specific embodiments presented herein.
[0025] FIGs. 1A-1E illustrate exemplary representation of a system for embedding digital information to secure an image, in accordance with an embodiment of the present disclosure.
[0026] FIG. 2 illustrates an exemplary flow diagram of a method to embed digital information to secure an image, in accordance with an embodiment of the present disclosure.
[0027] FIG. 3 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0028] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0029] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0030] The present disclosure relates, in general, to digital image watermarking, and more specifically, relates to a system and method for embedding digital information using kurtosis to secure a media object. The system and method can provide improved robust, image-adaptive watermarking scheme, two invisible image watermarks can be embedded in the media object to survive under different attacks, and can ensure copyright protection. The present disclosure can be described in enabling detail in the following examples, which may represent more than one embodiment of the present disclosure.
[0031] FIGs. 1A- 1E illustrate exemplary representation of a system for embedding digital information to secure an image, in accordance with an embodiment of the present disclosure.
[0032] Referring to FIG. 1A, system 100, (also referred to as digital watermarking system 100) can be configured to embed digital information to a media object, the digital information also interchangeably referred to as watermark, the media object can be any host image, the digital information can be embedded to improve security and to ensure copyright protection of the media object. The system 100 may include a computing device 102 configured to embed the digital information into the media object, the computing device 102 may include an information input unit 104, a processor 106, a memory 108, an encoder 110, a transmitter 112, display and input/output units. The image embedded with the digital information (watermarked image) can be transmitted to a receiver 114 over a transmission channel by means of wired or wireless communication/network. The receiver 114 may include a decoder 114 to extract the embedded digital information from the received image.
[0033] In an embodiment, the computing device 102 may include, for example, personal computer (PC), laptop computer, mobile terminal supporting suitable executable instructions for embedding the digital information on the target data. The system 100 may include a printer, connected to the computer device 102 by wired and/or wireless connection, the printer can be adapted to render the watermarked image on a suitable physical printable, typically paper. The information input unit 104 adapted to input the media object used may be either a digital camera or a pen drive, and digital information to be hidden in the media object. The media object is in this example can be two-dimensional host image.
[0034] In another embodiment, the transmitter 112 may include the information input unit 104 adapted to receive the image, the encoder 110 operatively coupled to the information input unit 104, the encoder 110 adapted to encode first bits of digital information and second bits of digital information onto the image. The receiver 114 operatively coupled to the transmitter 112 over the network, the receiver 114 adapted to receive the encoded information from the transmitter 112, the decoder 116 coupled to the receiver 116, the decoder 116 adapted to decode the encoded information received by the receiver 114. The processor 106 operatively coupled to the transmitter 112 and the receiver 114, the processor 106 coupled with a memory 108, the memory 108 storing instructions executable by the processor to receive, from the information input unit 104, the image, the processor 106 can be configured to segment, the received image into one or more blocks to select a high entropy block from one or more blocks.
[0035] The processor 106 can be configured to apply, Discrete Wavelet Transform (DWT) on each of the selected high entropy blocks, to determine one or more sub-bands of the image, the sub-bands can be low-low (LL), high-low (HL), low-high (LH) and high-high (HH). The processor 106 can select first sub-band and second sub-band from the one or more sub-bands to calculate, mean and variance of each of first sub-band and second sub-band. The first sub-band can be LL sub-band and the second sub-band can be LH sub-band. The adjustable strength factor (also referred to as adjustable strength factor (ASF)), for the first sub-band and second sub-band can be determined, wherein, upon determining the adjustable strength factor of the corresponding sub-bands, the processor 106 configured to encode, at the encoder 110, the first bits of digital information (also referred to as first watermark) onto the first sub-band and second bits of digital information (also referred to as second watermark) onto the second sub-band to ensure protection for the image, the segmented blocks are combined using the inverse DWT to create modified image. The receiver 114 can receive, the modified image from the transmitter 112, wherein, the modified image can be decoded at the decoder 116 to extract the encoded digital information from the corresponding sub-bands of the image.
[0036] In another embodiment, the decoder 116 can be configured to extract the modified image by segmenting the modified image into one or more blocks to select the high entropy block from the blocks. The DWT can be applied to each of the selected high entropy blocks of the received modified image to determine sub-bands of the image. The first sub-band and second sub-band can be selected from the one or more sub-bands of the modified image to calculate mean and variance using Gaussian distribution, wherein the decoder 116 can be configured to extract the first bits of digital information from the first sub-band, and extract the second bits of digital information from the second sub-band to obtain recovered image.
[0037] In statistics, moments are characterized as the tendency of a set of values to cluster around a particular value. The probability distribution of intensity levels helps to calculate four important statistical moments namely mean, standard deviation, skewness and kurtosis. Mean represents the average pixel of the image and is useful in determining the background of the image. The values of mean and standard deviation(s) are directly related to each other. The value of standard deviation is high if the available data is not close to the mean and vice versa. Both mean and standard deviation are called as the first and the second moments and are better known as lower order statistical parameters.
[0038] Skewness, or the third moment, helps to determine the asymmetry of a distribution centeredat its mean, it can be either positive or negative. Kurtosis can determine the shape of aprobability distribution, it is the fourth central moment and is represented in combination with noise and resolution measure. Both skewness and kurtosis are higher order statistics which can be used to determine more information of the shape. High positive values of kurtosis indicate images with low noise and less resolution. The value of kurtosis is very important as it helps to compare different blocks of the host image in the proposed watermarking approach. It is directly related to the ratio of the noise and resolution of an image. The different blocks may have same values of mean, standard deviation or skewness, yet different kurtosis values. The differences in DWT coefficients can be better understood with the help of kurtosis values for achieving a robust proposed watermarking technique. Finally, the positive values of kurtosis of each block too help in reducing the calculations and making them further easier. Thus, higher the statistical moments used, better are the measures of objective quality of the image in the process of watermarking.
Watermark embedding at encoder
[0039] In an exemplary embodiment, two invisible image watermarks can be embedded to the digital image by the encoder 110 for the purpose of copyright protection, security enhancement and improved robustness. In the theory of communication, entropy (H) is defined as the mean information per output source. The digital image represents different levels of entropy in itself, higher amounts of information are embedded in higher levels of entropy in the digital image. The human visual system (HVS) model is highly sensitive to less regions of entropy in the digital image which helps to embed large number of watermark bits imperceptibly.
[0040] The process of embedding the two watermarks can be illustrated in FIG. 1B, the processor 106 can be configured to receive, from the information input unit 102, the host image of size N × N, which may be divided into blocks of size 8 × 8. Here, N is the number of rows and N are the number of columns. The high entropy blocks can be selected from the blocks of size 8 × 8. Entropy is calculated for each block and the first moment i.e. mean entropy (or a reference) value is determined. This value helps to indicate the tendency of the coefficients to cluster around a threshold value in the given distribution. A cell array is formed which contains only those blocks, whose entropy is greater than the mean of entropy of all blocks. The order of such high entropy blocks can be formed by scanning each row one by one, which forms the first side information.
[0041] DWT can be applied on the selected high entropy blocks to obtain LL, LH, HL and HH frequency coefficients (also interchangeably referred to as sub-bands).Two-dimensional DWT is applied on high entropy blocks to obtain approximation (A), vertical(B), horizontal (C) and diagonal (D) frequency coefficients. LL sub-band contains a rough description of the image as it is the result of low-pass filtering of both the rows and columns, the LH sub-band is used to embed the watermark, as an acceptable performance of imperceptibility and robustness is achieved.
[0042] The approximation (LL) and vertical (LH) coefficients can be modelled by a Gaussian distribution. Generally, probability density function (PDF)is involved in Gaussian statistical approach in watermarking. However, an alternate approach to calculate parameters, mean (µ) and standard deviation (s) can be considered, this is the second important side information.
[0043] The first watermark and second watermark can be inserted simultaneously into LL and LH coefficients respectively. These coefficients can be varied in the upwards (for watermark bit‘1’) or the downward direction (for watermark bit ‘0’) by the adjustable value known as adjustable strength factor (ASF). It is calculated using kurtosis which is the fourth moment of statistics. This ASF value is used for inserting the bits of the watermarks and it can adjust in accordance with the image characteristics. The ASF is calculated using the fourth moment (kurtosis), the value of ASF is different for LL and LH coefficients.
[0044] The bits of the first image watermark can be embedded into the LL coefficients. The frequency coefficients of approximation sub-band (A) are modified by each bit (1 or 0) of the first watermark. The bits of the second image watermark can be embedded into the LH coefficients. Similarly, the frequency coefficients of horizontal sub-band (B) are also modified by each bit(1 or 0) of the second watermark
[0045] The prepared side information may include high entropy block positions, and means and variance of LL and LH coefficients. After embedding the first watermark and second watermark, inverse DWT can be applied on the modified blocks. The number of watermark bits to be hidden is always equal to the number of high entropy block selected, as a single bit is embedded in one block. The process of selecting the high entropy blocks is achieved by scanning each row one by one. Later, all modified high entropy blocks are recombined with the remaining blocks to form the watermarked image. The watermarked image is transmitted after combining all the blocks along with the side information.

Watermark extraction and statistical modelling of the decoder
[0046] The statistical approach for modelling the decoder 116 parameters helps to achieve accurate and robust results. The proposed decoder 116 as shown in FIG. 1C illustrates the following steps to describe the extraction of watermarks. The received watermarked image can be segmented into 8 × 8 blocks, the received watermarked image of size N × N is divided into blocks of size 8 × 8.Here, N is the number of rows and N are the number of columns. Select the high entropy blocks. The high entropy block positions sent in the side information are used to determine the highentropy blocks in the received watermarked image. Apply DWT on the selected high entropy blocks to obtain LL, LH, HL and HH frequency coefficients. Similar to the embedding process, two-dimensional DWT is applied on high entropy blocks of the received watermarked image to obtain approximation (A), vertical (B ), horizontal (C )and diagonal (D ) frequency coefficients
[0047] The LL and LH coefficients are modelled using mean and variance of Gaussian distribution, the mean (µ ) and variance (s 2) are again obtained as two main parameters from the LL and LH coefficients of the received watermarked image using Gaussian distribution as an assumption. Gaussian distribution parameters µ and s2 (transmitted inside information) are compared with modelling parameters µ and s2 of the received watermarked image. Optimum decoder 116 based upon the principle of maximum multiplicative embedding scheme is used for embedding watermark bit ‘1’ and ‘0’respectively in both approximation and horizontal coefficients for better robustness.
[0048] The watermarking technique implemented by the system 100 can uses side information to help the decoder 116 in successful recovery of the watermarks. This side information mainly comprises of firstly, the position of high entropy blocks and secondly, the means and variances of the (approximation and vertical) coefficients. The position of blocks with high entropy are in the form of an array and do not associate directly with the host image. The position of 8 × 8 blocks in the array are always temporary and not fixed which helps to ensure the security of the proposed technique to avoid any malicious attack. It becomes an important challenge for the attacker to estimate the position of the blocks with high entropy correctly. The security of the side information can be enhanced by encoding.
[0049] For example, four standard host images of size 512 × 512, namely, Lena, Barbara, Baboon and Peppers as illustrated in FIG. 1D can be considered. However, to prove the effectiveness of the proposed technique, a few simulation results were also obtained using host images of size 1024 × 1024. A total of four gray-scale digital image watermarks of different sizes were used in the experiment. Initially, a single image watermark TIET (18 × 25), Copyright (50 × 20) or Thapar Univ (80 × 20) can be embedded in the LL frequency coefficients of the standard host image. Later two image watermarks were simultaneously embedded in the LL and LH frequency coefficients respectively. The first image watermark can be any of the three different sizes mentioned above, whereas the second image watermark used can be Logo (64 × 64). Thus, a total of three watermark combinations can be used to insert the first and second image watermarks, namely (‘TIET’ and ‘Logo’), (‘Copyright’ and ‘Logo’) and (‘Thapar Univ and ‘Logo’).
[0050] In an example implementation, when two watermarks (TIET and Logo) are inserted simultaneously in each host image (in LL and LH sub-band respectively), the values of strength factor ? are different for embedding watermarks in each sub-band. Both the strength factors are varied together to achieve the best imperceptible watermarked image with minimum bit error rate (BER) (%) of recovered watermarks as illustrated in FIG. 1E. The values selected for strength factor(s) of approximation and vertical sub bands ? are (2.4,0.7),(2.6,0.9),(2.4, 0.9) and (2.4, 0.9) for host images Lena, Barbara, Peppers and Baboon respectively using ‘TIET’ (in LL sub-band) with ‘Logo’(in LH sub-band). Again for embedding ‘Copyright’and ‘Thapar Univ’(in LL sub bands)with Logo (in LH sub-band) in the same host images, the values of ? are grouped as (2.4,0.7), (2.6,0.9), (2.2,0.9), (2.4,0.9) and (2.4,0.7), (2.6,0.9), (2.2,0.9) and (2.2,0.9) respectively. The value of ASF lies in the range 4 × 10-3 to 7 × 10-2.The technique ensures watermarked images are highly imperceptible and robust with minimum BER.
[0051] Apart from PSNR and BER, NCC, SSIM, and gradient magnitude similarity deviation (GMSD) are additional quantitative metrics used in the study to determine the quality of the watermarked image. The ideal values of NCC and SSIM are unity which indicates the watermarked image is completely similar to the original image. GMSD uses pixel-wise gradient magnitude similarity or geometric structure approach to capture the local quality of the image. It uses standard deviation to average the diversity of local characteristics of the image which becomes highly relevant for subjective image quality. Since GMSD exhibits good linearity, so it proves a useful image quality assessment (IQA) parameter for real time applications. Ideally there must be no deviation between the watermarked and the original images.
[0052] Thus, the system 100 can maintain the invisibility and quality of the watermarked images, the process of watermark embedding and its recovery can be based on statistical modelling which helps to extract watermarks with better performance. The system 100 can evaluate the strength of the watermark effectively and can perform efficiently in terms of PSNR and NCC in comparison to the existing techniques.
[0053] FIG. 2 illustrates an exemplary flow diagram of a method to embed digital information to secure an image, in accordance with an embodiment of the present disclosure.
[0054] Referring to FIG. 2, the method 200 can be implemented using the computing device, which can include one or more processors. The method 200 can be configured to embed digital information to secure the image, at step 202, the method can include obtaining, at the information input unit, the image. At step 204, receiving, at the computing device, from the information input unit, the image, at step 206, segmenting, at the computing device, the received image into one or more blocks to select the high entropy block from the one or more blocks. At step 208, applying, at the computing device, Discrete Wavelet Transform (DWT) on each of the selected high entropy blocks, to determine one or more sub-bands of the image, at step 210 selecting, at the computing device, first sub-band and second sub-band from the plurality of sub-bands to calculate mean and variance for each of the first sub-band and second sub-band.
[0055] At step 212, determining, at the computing device, adjustable strength factor, for each of the first sub-band and second sub-band, wherein, at step 214, upon determining the adjustable strength factor of the corresponding selected sub-bands, the computing device configured to encode, at the encoder operatively coupled to the information input unit, the first bits of digital information onto the first sub-band and second bits of digital information onto the second sub-band to ensure protection of image, wherein, the modified image is obtained by combining the segmented plurality of blocks using the inverse DWT. At step 216 receiving, at the receiver, the modified image, wherein, the modified image is decoded, at the decoder coupled to the receiver, to extract the encoded digital information from the corresponding selected sub-bands of the image.
[0056] In another embodiment, blocks with high entropy are used for inserting the watermark bits. These blocks help to calculate ASF using kurtosis, kurtosis is considered as a measure of sparsity which helps to determine whether the approximation (or vertical) 8 × 8 block coefficients of the host image form a flat or a peaked distribution. This peakness is associated with the perceptual aspects of sparse coding. For a normal distribution, Kurtosis values are zero. All values of kurtosis greater than three indicate flat-tailed distribution (Leptokurtic), whereas values lesser than three indicate thin tailed (Platy-kurtic) distributions. While in former majority of the information lies in the tail itself, on the other hand it lies more in the peak rather than the tail for the latter.
[0057] The kurtosis values for watermark ‘TIET’ (450 bits) lies in the range 1 to 4.7 and 1.4 to 2.8 for host images Lena and Barbara respectively. Similarly, for watermarks ‘Copyright’ (1000 bits) and ‘Thapar Univ’(1600 bits), the range lies between 1.5 to 4.5 for Baboon and 1 to 5 for Peppers respectively. This helps in calculating ASF, the decision process to select suitable blocks is based on kurtosis. The maximum value of kurtosis of a selected block indicates the presence of noise. This helps to calculate ASF for embedding which uses the ratio of kurtosis of each block to the value of maximum kurtosis. This approach prevents the probable loss on the watermark bits and helps to prove the robustness of the proposed watermarking scheme. Kurtosis, being the fourth order statistical parameter, offers the advantage of being invariant to luminance changes in images and exhibits generally centred and homogenous variations. Thus, it is preferred for image watermarking though the use of the same in embedding two watermarks reduces the value of desirable PSNR.
[0058] In another embodiment, the present disclosure may prove useful in embedding the details of patient report (first watermark) and the personal characteristics, such as, photograph of the patient or the doctor (second watermark) into the diagnosis (host image). Thus, the concept of dual watermarking seems successful in preserving the special characteristics of medical images. Normally a patient does not like to expose his/her organ’s medical image to public. The utmost security and confidentiality can be maintained by watermarking the medical image and its related text in the single image. The patient information is embedded along with ‘Joint’ (size 32 × 32) in the medical hostimage. The patient watermark includes details of doctor, hospital code, image number and information of the patient diagnosis. When the patient data text and image are sent separately, any kind of tampering done on the text or image may produce an incorrect diagnosis. This may causeserious repercussions to the extent of even costing a life. Thus, dual watermarking approach can help in maintaining the confidentiality of the patient details and reducing the cases of medical thefts.
[0059] FIG. 3 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
[0060] As shown in FIG. 3, computer system 300 includes an external storage device 310, a bus 320, a main memory 330, a read only memory 340, a mass storage device 350, communication port 360, and a processor 370. A person skilled in the art will appreciate that computer system may include more than one processor and communication ports. Examples of processor 370 include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on a chip processors or other future processors. Processor 370 may include various modules associated with embodiments of the present invention and may be configured with MATLAB. Communication port 360 can be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fibre, a serial port, a parallel port, or other existing or future ports. Communication port 360 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[0061] Memory 330 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read only memory 340 can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor 370. Mass storage 350 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7200 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0062] Bus 320 communicatively couples processor(s) 370 with the other memory, storage, and communication blocks. Bus 320 can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor 370 to software system.
[0063] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus 320 to support direct operator interaction with computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 360. External storage device 310 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Writable (CD-RW), Digital Video Disk - Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0064] It will be apparent to those skilled in the art that the apparatus 100 of the disclosure may be provided using some or all the mentioned features and components without departing from the scope of the present disclosure. While various embodiments of the present disclosure have been illustrated and described herein, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0065] The present disclosure provides a system that can maintain the invisibility and quality of the watermarked images, the process of watermark embedding and its recovery is based on statistical modelling which helps to extract watermarks with better performance.
[0066] The present disclosure provides a system that can evaluate the strength of the watermark effectively.
[0067] The present disclosure provides a system that can perform efficiently in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC) of watermarked images and Bit Error Rate (BER) of recovered watermark(s) in comparison to the existing techniques.
[0068] The present disclosure provides a system in which the utmost authenticity is maintained by watermarking the patient medical image with limited capacity and its related text in the single image. This helps to reduce the chances of wrong diagnosis which otherwise can even cost a life.
[0069] The present disclosure provides a system that can improve the balance of robustness, capacity and imperceptibility by the quality assessment parameters such as PSNR, NCC, SSIM, GMSD of watermarked images and BER of recovered watermarks.
[0070] The present disclosure provides a system that can overcome various attacks in healthcare sector and the like.
[0071] The present disclosure provides a system that can maintain the confidentiality of the patient details and can reduce the cases of medical thefts.