ANALYSIS OF DIFFERENT DOMAIN WATERMARKING TECHNIQUES 1 Maneet, 2 Prabhjot Kaur 1 Assistant Professor, AIMT/ EE Department, Indri-Karnal, India Email: maneetkaur122@gmail.com 2 Assistant Professor, AIMT/ ECE Department, Indri-Karnal, India Email: prabhjot004@gmail.com Abstract Due to improvement in imaging technology and the ease with which digital content can be reproduced and manipulated there is a strong need for a digital copyright mechanism to be put in place. Digital Watermarking is being seen as a potential solution to this problem. Digital watermarking is a technology which embeds a watermark signal into the host image to authenticate it. It deals with hiding secret bits of information with a digital content as a cover. This is very useful for many applications like copyright control, ownership identification, authenticity of documents, captioning, broadcast monitoring, Transaction tracking etc. Various techniques for watermarking have been proposed in the recent past. The discussion of some techniques and their results are presented in this paper which forms the basis of comparison between these techniques. Keywords:, DWT, Digital Watermarking, Watermarking Embedding Procedure, Watermarking Extraction Procedure. 1. INTRODUCTION With the ever-growing expansion of digital multimedia and the internet digitizing of visual data such as images and video has become popular.however,this advancement in technology has double impact.first impact is that,it has permitted faster and more efficient storage,transfer and processing lf digital data. The second impact is, duplication and manipulation of digital contents has also become very easy and undetectable,which allows fast and error free movement of any unauthorized digital data and possibly manipu lated copy of such information, grow in popularity in the recent years, security concerns over copyright pro tection of digital multimedia data have also been increasingly emphasized. One of the most promising solutions appears to add author information (watermark) into the visual data as a secondary signal that is not perceivable and is bonded so well with the o riginal data that it is undividable. Techniques to embed and recover such secondary information or stamps (called watermark) is digital watermarking. 2. DIGITAL WATERMARKING Digital Watermarking is a technology of embedding watermark with intellectual property rights into images, videos, audios and other multimedia data by a certain algorithm. This kind of watermark contains the author and user s information, which could be the owner s logo, serial number or control information [1]. Digital Watermarking is very common in our daily lives; watermarking in currency, government documents, stamps, and many other common document detection of the watermark. Watermarking has found use in many application fields: copyright protection, co ntent indexing, data monitoring and tracking, and data authentication. In next subsections we discuss the watermark embedding and extraction procedure, different domains for watermark embedding and parameters to evaluate the performance of a watermarking technique. 2.1 Watermark Embedding and Extraction A watermarking algorithm embeds a visible or invisible watermark in a given multimedia object. In the watermarking embedding scheme, the input to the scheme is watermark, the cover image data and optional or secret key. The watermark can be any nature, such as a number, text, an image. The secret or public key is used to enforce the security, 115 P a g e IJRREST h t t p : / / i j r r e s t. o r g / i s s u e s /? p a g e _ i d = 1 2
Figure. 1: Watermarking Embedding For the detection scheme the input is watermarked image, watermark image and optional or secrete key and depends upon the original data and original watermark. Figure. 2: Watermarking Extraction The output of the watermark recovery process is either the recovered watermark or some kind of confidence measure indicating how likely it is for the given watermark image at the input to be present in the data un der inspection. 2.2 Watermarking Domains To transform an image to its frequency representation, one can use several reversible transforms like Discrete Cosine Transform (), Discrete Wavelet Transform (DWT), or Discrete Fourier Transform (DFT). Each of these transforms has its own characteristics and represents the image in different ways. In the DWT, an image signal can be analyzed by passing it through an analysis filter bank followed by decimation operation. The analysis filter bank consists of a low pass and high pass filters act each decomposition stage. The DWT (Discrete Wavelet Transform) separates an image into a lower resolution approximation image (LL) as well as horizontal (HL), vertical (LH) and diagonal (HH) detail components. The process can then be repeated to computes multiple scale wavelet decomposition LL2 HL2 HL1 LH 2 HL2 LH1 HH1 Figure. 3: 2 Scale 2 Dimensional DWT The is a linear transform which maps an n-dimensional vector to set of n coefficients. The allows an image to be broken up into different frequency bands, making it much easier to embed watermarking information into the middle frequency bands of an image. The middle frequency bands are chosen such that they have minimize they avoid the most visual important parts of the image (low frequencies) without over-exposing themselves to removal through compression and noise attacks (high frequencies) One su ch technique utilizes the 116 P a g e IJRREST h t t p : / / i j r r e s t. o r g / i s s u e s /? p a g e _ i d = 1 2
comparison of middle-band coefficients to encode a single bit into a block. To begin, we define the middle-band frequencies (FM) of an 8x8 block as shown in figure 4 FL FL FL FM FM FM FM FH FL FL FM FM FM FM FH FH FL FM FM FM FM FH FH FH FM FM FM FH FH FH FH FH FM FM FM FH FH FH FH FH FM FM FH FH FH FH FH FH FM FH FH FH FH FH FH FH FH FH FH FH FH FH FH FH Figure. 4: block. FL is used to denote the lowest frequency components of the block, while FH is used to denote the higher frequency components. FM is chosen as the embedding region as to provide additional resistance to lossy compression techniques, while avoiding significant modification of the cover image [9]. The Fourier Transform DFT is an important image processing tool which is used to decompose an image into its sine and cosine components. DFT of a real image is generally complex valued, which results in the phase a nd magnitude representation of an image. The difference between and DWT is that DFT applies to complex numbers, while uses just real numbers. For real input data with even symmetry and DFT are equivalent. The strongest components of the DFT are the central components which contain the low frequencies. DFT is rotation, scaling and translation (RST) invariant. Hence it can be used to recover from geometric distortions, whereas the spatial domain, and the DWT are not RST invariant and hence it is difficult to overcome from geometric distortions [9]. 2.3 Evaluation parameters of a watermarking technique 2.3.1Mean Square Error The mean square error between the original image f (m, n) and the reconstructed image g (m, n) is given by MSE= ) ) ) Here N M represents the size of the image. The MSE is a very useful measure as it gives an average value of the energy lost in lossy compression of the original image. A human observing two images affected by the same type of degradation will generally judge the one with smaller MSE to be closer to the original. A very small MSE can be taken to mean that the image is very closer to the original. Original image and reconstructed image (watermarked image) are the grayscale of the images to compare to measure value of the energy lost in lossy compression of the original image. 2.3.2 Peak Signal to Noise Ratio A more subjective qualitative measurement of distortion is the peak signal to noise ratio. The peak signal to noise ratio (PSNR) is used to evaluate the image quality by calculating the mean square error(mse) between the images to compare. PSNR=10 For an 8-bit image, b=8 which gives the PSNR value as PSNR=10 The PSNR is expressed in db. The PSNR evaluation parameter is superior measurement method. It uses a constant value in which to compare the noise against instead of a fluctuating signal. This allows PSNR values received to be treated more meaning fully when quantifiably comparing different image coding algorithms. For this reason, PSNR is used throughout the image coding as a valid way to compare image coding algorithms. We calculate PSNR between original image and watermarked image that we output from the embedding process. The higher the PSNR shows the better quality of watermarked image. So, if we have bigger PSNR, it shows least difference between original image and watermarked image that shows more robustness against attacks. ) ] ) ] 117 P a g e IJRREST h t t p : / / i j r r e s t. o r g / i s s u e s /? p a g e _ i d = 1 2
2.3.3 Correlation Correlation is an extremely powerful technique for finding similarities between the watermarked image and cover image. The correlation is one of the most comm0n and most useful statistics. A correlation is a single number that describes the degree of relationship between the watermarked image and cover image. The symbol r is stand for the correlation. Through the magic 0of mathematics it turns out that r will always be between -1.0 and +1.0. If the correlation is negative, we have a negative relationship. Computes the correlation coefficients original image f (m, n) and the reconstructed image g (m, n) is given by r=corr2 (f (m, n), g (m, n)) Corr2 computes the correlation coefficients using r= ) )) ) )) ) ) ) ) ) ) The accuracy rate AR is defined as AR= Where NP is the number of pixels of the watermark image and CP is the number of correct pixels in the watermark image that is retrieved from the attacked image. 3. WATERMARKING TECHNIQUES In [1] a spatial watermarking technique for gray scale images.the algorithm is implemented by pixel by pixel comparison between host image and watermark. For the each pixel of host image if its correspond value equal to compared pixel in watermark, we save its position as key. First two positions of key vector are the dimensions of watermark. For watermark extraction, first, we create m n (size of watermark) matrix. Then we fill this matrix with values from host image that their positions are saved in key.after implementation of extraction algorithm we compare extracted watermark and original Watermark. The algorithm was found to be robust against many attacks. In [6] a new watermarking scheme was proposed for JPEG images using modified. The advantage is that it minimizes blocking artifacts which are common JPEG images, thus improving the visual quality. It is robust against many attacks. The for a one dimensional array is defined as: = (N+ ) K=0, 1 N-1 One dimensional inverse discrete cosine transformation )] K=0, 1 N-1 For watermark embedding we embed a binary image as watermark into the higher frequency Components in Modified using a random key. Then a inverse transformation is applied to obtain the final watermarked image. In [3] a watermarking technique in transform domain was proposed by taking DWT of a image and adding PN sequences to H1 and V1 components of the image. In the DWT, an image signal can be analyzed by passing it through an analysis filter bank followed by decimation operation. The analysis filter bank consists of a low pass and high pass filters act each decomposition stage The DWT (Discrete Wavelet Transform) separates an image into a lower resolution approximation image (LL) as well as horizontal (HL), vertical (LH) and diagonal (HH) detail components One of the many advantages over the wavelet transform is that it is believed to more accurately model aspects of the HVS as compared to the FFT or. This allows us to use higher energy watermarks in regions that the HVS is known to be less sensitive to, such as the high resolution detail bands {LH, HL, and HH). Embedding watermarks in these regions allow us to increase the robustness of our watermark, at little to no additional impact on image quality. The process can then be repeated to computes multiple scale wavelet decomposition, as in the 2 scale wavelet transform The proposed method was implemented in MATLAB and technique is proved to be resistant to JPEG compression, cropping. 118 P a g e IJRREST h t t p : / / i j r r e s t. o r g / i s s u e s /? p a g e _ i d = 1 2
In [4] a collusion attack resistant watermarking scheme for Colored images using. It improved on the classical middle band coefficient Exchange watermarking scheme by averaging of middle frequency coefficients of the image. In this scheme we choose any 4 coefficients from the FM region of its block Calculate th e average of 18 middle band coefficients,hide 0 for all 4 chosen coefficients assign the value of coefficients which is less than the average, if the average is greater than the coefficients value hide 1. In [5] lossless watermarking scheme was suggested which applied fuzzy integral to find similarity between coefficients of original image and watermark. For each block in watermark image, the best match block in the original image is selected by applying fuzzy integral and the corresponding block number is kept as secret key. The watermark extraction of the proposed method uses secret key to extract watermark and it does not require the original image. 4. COMPARISION OF WATERMARKING TECHNIQUES In the given table 1 below the watermarking techniques are compared. Table I: Watermarking Techniques Name of Technique Domain Resistance against attacks Host Image Advantages of using this technique Disadvantages of using this technique Spatial domain algorithm for grey scale images Spatial Gaussian noise, Weiner filtering, compression Scale Simple and east to implement Less robust against Gaussian noise attack, not used for color images An improved digital watermarking technique for JPEG protection A DWT Domain Visible Watermarking Techniques for Digital Images[4] Collusion Attack Resistant Watermarking Scheme for Colored Images using A new Lossless Watermarking Scheme Based on Fuzzy integral and DWT Filtering, Rotation, Scaling Compression Collusion attack Compression, Gaussian noise, Median filtering Scale Scale Color Images scale Improves the visual quality by eliminates blocking artifacts Using DWT it is more accurately model aspects of HVS as compared to FFT and Specially designed for collusion attack A blind watermarking technique which find usage in real applications M and I is complex than Not robust against various attacks Finding suitable color channel for watermark embedding not easy Computation Overhead 5. CONCLUS ION In this paper, we described the digital watermarking embedding and extraction technique. Then we presented some digital watermarking techniques implemented in different domains and compared these techniques. These techniques were found to be robust against many attacks. Future work can be done to study more watermarking techniques and find a new watermarking technique which is robust against attacks. 119 P a g e IJRREST h t t p : / / i j r r e s t. o r g / i s s u e s /? p a g e _ i d = 1 2
7. REFERENCES [1] Yanqun Zhang Digital Watermarking Technology: A Review 2009 ETP International Conference on Future Computer and Communication. [2] Houtan Haddad Larijani and Gholamali Rezai Rad A New Spatial Domain Algorithm for gray scale images watermarking Proceedings of the International Conference on Computer and Communication Engineering 2008 May 13-15, 2008 Kuala Lumpur, Malaysia. [3] Vikas Saxena, J.P Gupta Collusion Attack Resistant Watermarking Scheme for Colored Images using IAENG International JournalofComputerScience,34:2,IJCS_34 _2_02. [4] Reza Mortezaei, Mohsen Ebrahimi A new lossless watermarking Scheme based on fuzzy integral and domain 2010 International Conference on Electronics and Information Engineering(ICEIE 2010). [5] Afzel Noore An Improved Digital Watermarking Technique for Protecting JPEG images WPM P2.08 0-7803-7721-4/ 03 2003 IEEE. 120 P a g e IJRREST h t t p : / / i j r r e s t. o r g / i s s u e s /? p a g e _ i d = 1 2