A Quantization based blind and Robust Image Watermarking Algorithm by ijcsis

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									                                                               (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                               Vol. 9, No. 2, February 2011

          A Quantization based blind and Robust Image
                   Watermarking Algorithm
                                                         Mohamed M. Fouad
                 Electronics and Communication Department- Faculty of Engineering- Zagazig University- Egypt
                                                  fouadzu@hotmail.com



Abstract—Security and privacy issues of the transmitted data                multi-media object. The embedding process is guided by use
have become an important concern in multimedia technology.                  of a secret key, which decides the locations within the
Watermarking which belong to the field of information hiding                multimedia object (image) where the watermark would be
has seen a lot of research interest recently. Watermarking is used          embedded. Once the watermark is embedded it can experience
for a variety of reasons including security, content protection,            several attacks because the multimedia object can be digitally
copyright      management,       trust    management,       content
authentication, tamper detection and privacy. Recently many
                                                                            processed. The attacks can be unintentional (in the case of
watermarking techniques have been proposed to support these                 images, low pass filtering or gamma correction or
applications but one major issue with most of the watermarking              compression) or intentional (like cropping). Hence, the
techniques is that these techniques fail in the presence of severe          watermark has to be very robust against all these possible
attacks. This has been a major threat to content providers                  attacks. When the owner wants to check the watermarks in the
because if the digital content is dramatically changed then it              possibly attacked and distorted multimedia object, s/he relies
would be difficult to prove the existence of a watermark in it and          on the secret key that was used to embed the watermark. Using
consequently its ownership. To tackle this security threat towards          the secret key, the embedded watermark sequence can be
ownership issues in this paper, we propose a computationally                extracted. This extracted watermark may or may not resemble
efficient and secure two quantization based watermarking
algorithms which offer incredible performance in presence of
                                                                            the original watermark, because the object might have been
malicious attacks which try to remove ownership information.                attacked.
The performance of the proposed techniques is compared with                      Hence, to validate the existence of a watermark, either the
that of other watermarking techniques and it gives a very good
                                                                            original object is used to compare and ascertain the watermark
perceptual quality especially at lower bit rates. We present
experimental results which show that the proposed techniques
                                                                            signal (non-blind watermarking), or a correlation measure is
outperform many techniques for multimedia over wireless                     used to detect the strength of the watermark signal from the
applications. The proposed schemes are backed up with excellent             extracted watermark (blind watermarking). In correlation
results.                                                                    based detection, the original watermark sequence is compared
                                                                            with the extracted watermark sequence, and a statistical
  Keywords-component; Watermark Detection; Watermarking;
DCT; DWT; Quantization
                                                                            correlation test is used to determine the existence of the
                                                                            watermark.
                      I. INTRODUCTION
                                                                            A. Requirements of Digital Watermarking
     Watermarking is a method of hiding proprietary
                                                                                There are three main requirements of digital
information in digital media like photographs, digital music, or
                                                                            watermarking. They are transparency, robustness and
digital video. The ease with which digital content can be
                                                                            capacity.
exchanged over the Internet has created copyright
infringement issues. Copyrighted material can be easily                          Transparency or Fidelity, The digital watermark should
exchanged over peer-to-peer networks, and this has caused                   not affect the quality of the original image after it is
major concerns for those content providers who produce these                watermarked. Cox et al. (2002) defines transparency or fidelity
digital contents. In order to protect the interest of the content           as ‘perceptual similarity between the original and the
providers these digital contents can be watermarked.                        watermarked versions of the cover work’ [1]. Watermarking
                                                                            should not introduce visible distortions because if such
    The process of embedding a watermark in a multimedia
                                                                            distortions are introduced it reduces the commercial value of
object is termed as watermarking. A Watermark can be
considered as a kind of a signature, which reveals the owner of             the image.
the multimedia object. Content providers want to embed
watermarks in their multimedia objects (digital content) for
several reasons like copyright protection, content
authentication, tamper detection etc. A watermarking
algorithm embeds a visible or invisible watermark in a given



                                                                      241                              http://sites.google.com/site/ijcsis/
                                                                                                       ISSN 1947-5500
                                                            (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                            Vol. 9, No. 2, February 2011
     Robustness, Cox et al. (2002) defines robustness as the                  slice has a width that is inversely proportional to its height.
‘ability to detect the watermark after common signal                          The number of these slices is equal to the number of
processing operations’ [1]. Watermarks could be removed                       quantization levels.
intentionally or unintentionally by simple image processing
                                                                         2. On the horizontal axis of the sliced histogram, each slice
operations like contrast or brightness enhancement, gamma
                                                                            has start and end points. The midpoint value (on the width)
correction etc. Hence watermarks should be robust against a
                                                                            of each slice is considered as a quantization level.
variety of such attacks into four basic categories, attacks that
try to remove watermarks totally, attacks that try to remove             3. In this way, we get a non-uniform quantization in which
the synchronization between the embedder and the detector,                  the density of the quantization levels increases in
cryptographic attacks and protocol attacks.                                 proportion to the probability of occurrence of the pixel
                                                                            value.
     Capacity or Data Payload, Cox et al. (2002) define
capacity or data payload as ‘the number of bits a watermark              4. All the pixel values that lie within the width of a slice are
encodes within a unit of time or work’ [1]. This property                   mapped to the quantization level that is represented by the
describes how much data should be embedded as a watermark                   midpoint of this slice.
to successfully detect during extraction. Watermark should be               The resultant compression ratio and signal-to-noise ratio
able to carry enough information to represent the uniqueness             vary depending on the chosen number of quantization levels.
of the image. Different applications have different payload
requirements [1].                                                            This technique is irreversible, i.e. the quantized values
                                                                         can’t be converted back to their original values leading to
     Security, according to Kerckhoff’s principle the security           information loss.
of a cryptosystem depends on the secrecy of the key and not
on the cryptographic algorithm. Same rule applies to water-                   III. DCT PROPOSED WATERMARKING TECHNIQUE
marking algorithms, i.e. the watermarking algorithms must be
public but watermark embedding should base on a secret key                    The first proposed watermarking scheme is a blind
[2].                                                                     quantization based scheme [4]. A block diagram detailing its
                                                                         steps is shown in Fig. 1. The input N*M image; an image
     To prevent image manipulations and fraudulent use of                assumed to be a matrix has length of N rows and width of M
modified images, the watermark should survive modifications                columns, is first converted into single vector by concatenating
introduced by random noise or compression, but should not be             successive rows beside each other to form a long row that
detectable from non-authentic regions of the image. The                  contains all the image pixels using matrix to vector converter.
original image cannot be used by the watermark detect or to              This vector is exposed to DCT [5]-[7] to transform the image
verify the authenticity of the image. In this paper, we                  from spatial domain into frequency domain in which energy of
investigate the application of a recently developed                      the image information is concentrated in a few number of
quantization based watermarking scheme to image                          coefficients. The output of the DCT process is a vector that
authentication. The two proposed watermarking techniques                 has the same length of the image) number of pixels in the
allow reliable blind watermark detection from a small number             image), but with many values approximated to zeros. After
of pixels, and thus enable the detection of local modifications           applying the DCT the output coefficients are arranged in a
to the image content.                                                    descending order according to the pixels probabilities. The
                                                                         output vector of the DCT is now ready to be processed by the
        II. HISTOGRAM EQUAL AREA DIVISION                                histogram equal area quantization technique to choose the
              QUANTIZATION TECHNIQUE                                     appropriate values used in the watermark embedding process,
                                                                         quantization levels. The watermarked coefficients vector is
     The technique calculates the quantization levels using a            reshaped and returned back to the spatial domain using IDCT.
method that is dependent on the image content (hence the
word "adaptive") and then round off the pixels values to the
nearest quantization level. In this way, the number of
transmitted values is reduced. The quantization scheme
provides a wide range of compression ratios (CRs) with a very
slight degradation of the signal-to-noise ratio (SNR).
     HEAD is a quantization technique in which the
transmitted values are reduced by mapping the values of                         Figure 1. The first proposed image watermarking scheme.
image pixels to a finite number of quantization levels.                  A.    Watermark Embedding
The HEAD quantization procedure can be listed as follows:
1. The area under the histogram of the image pixels is divided           The steps of watermark embedding can be summarized as
   into a number of vertical slices with equal areas. Thus each          follows:




                                                                   242                                http://sites.google.com/site/ijcsis/
                                                                                                      ISSN 1947-5500
                                                                    (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                    Vol. 9, No. 2, February 2011
1.    The host image is transformed into the DCT domain; the                     No watermark was inserted into the low-pass sub-band. Unlike
      transformed coefficients are watermarked using HEAD                        some non-blind watermarking schemes [9][10], this scheme
      quantization using 4 quantization levels t0, t1, t2, and t3.               allows a watermark to be detected without access to the
2.    A binary watermark of the same size as the image of                        original image. It performs an implicit visual masking as only
      interest is created using a secret key, which is a seed of a               wavelet coefficients with large magnitude are selected for
      random number generator.                                                   watermark insertion. These coefficients correspond to regions
3.    Each
              s
             wij of the selected DCT Coefficients is quantized.                  of texture and edges in an image. This scheme makes it
                                                                                 difficult for a human viewer to perceive any degradation in the
      The quantization process can be summarized as follows:                     watermarked image. Also, because wavelet coefficients of
                                's
If xij = 1 and
                  s
                 wij > 0, then wij = t2,                                         large magnitude are perceptually significant, it is difficult to
                                                                                 remove the watermark without severely distorting the
                                's
If xij = 0 and
                  s
                 wij > 0, then wij = t1,                                         watermarked image. The most novel aspect of this scheme was
                                's
                                                                                 the introduction of a watermark consisting of pseudorandom
                  s
If xij = 1 and   wij < 0, then wij = -t3,                                        real numbers. Since watermark detection typically consists of
                  s             's                                               a process of correlation estimation, in which the watermark
If xij = 0 and   wij < 0, then wij = -t0.                     (1)                coefficients are placed in the image, changes in the location of
                                                                     '           the watermarked coefficients are unacceptable. The
Where xij the watermark is bit corresponding to wij , and
                                                         s
                                                                    wijs         watermarking scheme proposed by Dugad et al. is based on
is the watermarked coefficient. After all the selected                           adding the watermark in selected coefficients with significant
coefficients are quantized, the inverse discrete cosine                          energy in the transform domain in order to ensure the non-
transform (IDCT) is applied and the watermarked image is                         erasability of the watermark. This scheme has overcome the
obtained.                                                                        problem of “order sensitivity”.
B. Watermark Detection                                                              Unfortunately, this scheme has also some disadvantages. It
                                                                                 embeds the watermark in an additive fashion. It is known that
1.    The possibly corrupted watermarked image is transformed                    blind detectors for additive watermarking schemes must
      into the DCT domain as in the embedding process.                           correlate the possibly watermarked image coefficients with the
2.    The extraction is performed on the coefficients.                           known watermark in order to determine if the image has or has
3.    All the coefficients of magnitude equal to t1, t2, - t3 and - t0           not been marked. Thus, the image itself must be treated as
                                            's
      are selected; these are denoted wij .The watermark bits                    noise, which makes the detection of the watermark
                                                                                 exceedingly difficult [8]. In order to overcome this problem, it
      are extracted from each of the selected DCT coefficients
                                                                                 is necessary to correlate a very large number of coefficients,
      with Eq.2. Fig. 2 illustrates the watermark detection
                                                                                 which in turn requires the watermark to be embedded into
      process.
                                                                                 several image coefficients at the insertion stage. As a result,
                                                                                 the degradation in the watermarked image increases. Another
                                                                                 drawback is that the detector can only tell if the watermark is
                                                                                 present or not. It cannot recover the actual watermark.

                                                                                    The scheme in [11] is another example of wavelet-based
                                                                                 watermarking schemes. A noise-like Gaussian sequence is
        Figure 2. Watermark detection in the proposed scheme.                    used as a watermark. To embed the watermark robustly and
         's                                                                      imperceptibly, watermark components are added to the
     If wij = t2 or t3, then the recovered watermark bit is a 1.                 significant coefficients of each selected sub-band by
        's                                                                       considering the human visual system (HVS) characteristics.
   If wij = t0 or t1, then the recovered watermark bit is a 0                    Some small modifications are performed to improve the HVS
                                (2)                                              model. The host image is needed in the watermark extraction
4. The recovered watermark is then correlated with the                           procedure.
    original watermark in the watermark file, obtained via the
    secret key. This allows a confidence measure to be
                                                                                   V. PROPOSED DWT WATERMARKING TECHNIQUE
    ascertained for the presence or absence of a watermark in
    an image.                                                                         Discrete wavelet transform is a technique using which a
                                                                                 2D image can be transferred from spatial domain to frequency
          IV. DWT WATERMARKING TECHNIQUE                                         domain. The input N*M image; an image assumed to be a
                                                                                 matrix has length of N rows and width of M columns, is
   Dugad et al. presented a blind additive watermarking                          exposed to wavelet transform. After one level DWT an image
scheme operating in the wavelet domain [8]. Three-level                          I is decomposed into four subbands LL, HL, LH, and HH. LL
wavelet decomposition with Daubechies 8-tap filters was used.                    is called the approximate band and it contains most of the




                                                                           243                              http://sites.google.com/site/ijcsis/
                                                                                                            ISSN 1947-5500
                                                               (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                               Vol. 9, No. 2, February 2011
energy. In the algorithm we decompose the image into four                 4.    After all the selected coefficients are quantized, the
levels and embed the watermark in HL, LH sub-bands. Here                        inverse discrete wavelet transform (IDWT) is applied and
we assume the size of the watermark logo is in multiple of the                  the watermarked image is obtained. 
sub-band size. In the second proposed a quantization based
                                                                          B. Watermark Detection
watermarking algorithm, we incorporate implicit visual
masking by embedding the watermark in the LH, HL sub-
bands. The output vector of the wavelet is now ready to be                1. The possibly corrupted watermarked image is
processed by the histogram equal area quantization technique              transformed into the wavelet domain using the same
to choose the appropriate values used in the watermark                    wavelet transform as in the embedding process.
embedding process, quantization levels. The watermarked                   2. The extraction is performed on the coefficients in the first
coefficients vector is reshaped and returned back to the spatial          level wavelet transform (excluding the LL1 subband).
domain using IDWT.                                                        3. All the coefficients of magnitude equal to t1, t2, - t3 and - t0
                                                                                                                           's
                                                                          are selected; these are denoted wij .The watermark bits are
                                                                          extracted from each of the selected DCT coefficients with
                                                                          Eq.4. Fig. 4 illustrates the watermark detection process.




           Figure 3. The proposed image watermarking scheme.                       Figure 4. Watermark detection in the proposed scheme.
                                                                                   's
                                                                               If wij = t2 or t3, then the recovered watermark bit is a 1.
A.   Watermark Embedding
                                                                                    's
                                                                                If wij = t0 or t1, then the recovered watermark bit is a 0
The steps of watermark embedding can be summarized as                                                        (4)
follows:
1. The host image is transformed into the wavelet domain; one             4. The recovered watermark is then correlated with the
     level Daubechies wavelet with filters of length 4 is used.                original watermark in the watermark file, obtained via the
     The coefficients (excluding the LL1 and HH1)                              secret key. This allows a confidence measure to be
     coefficients are watermarked using HEAD quantization                      ascertained for the presence or absence of a watermark in
     using 4 quantization levels t0, t1, t2, and t3.                           an image.
2. A binary watermark of the same size as the subbands of                 5. The recovered watermark is then correlated with the
     interest is created using a secret key, which is a seed of a             original watermark in the watermark file, obtained via the
     random number generator.                                                 secret key, only in the locations of the selected
3. Each
            s
           wij of the selected wavelet coefficients is quantized.             coefficients. This allows a confidence measure to be
                                                                              ascertained for the presence or absence of a watermark in
     The quantization process can be summarized as follows:                   an image.
                       s             's
     If xij = 1 and   wij > 0, then wij = t2,                                       VI. PERCEPTUAL QUALITY METRICS
                       s         's
     If xij = 0 and w > 0, then wij = t1,
                       ij                                                 Two metrics for ascertaining the quality of a watermarked
                       s               's                                 image are highlighted in this section. These metrics are the
     If xij = 1 and   w < 0, then w = -t3,
                       ij              ij
                                                                          Mean Square Error (MSE), and the Peak Signal to Noise Ratio
                                                    '
     If xij = 0 and
                             s
                            wij   < 0, then        wijs    = -t0.         (PSNR). The MSE measures the average pixel-by-pixel
                                                                          difference between the original image (I) and the watermarked
     (3)
                                                                                  ˆ
                                                                          image ( I ) [12].
                                                                                        1                          (5)
                                                           s
     Where xij the watermark is bit corresponding to wij , and                 MSE =      ∑ (I m,n − Iˆm,n )2
                                                                                       MN m,n
       '
      wijs is the watermarked wavelet coefficient. Figure (3)
                                                                                                                 2
                                                                                                             I            (6)
      shows the watermark embedding in a positive wavelet                 PSNR ( dB ) = 10 log          10
                                                                                                                 peak

      coefficient.                                                                                           MSE




                                                                    244                                          http://sites.google.com/site/ijcsis/
                                                                                                                 ISSN 1947-5500
                                                           (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                           Vol. 9, No. 2, February 2011
Where Ipeak is the peak intensity level in the original image
(most commonly 255 for an 8-bit grayscale image), M and N
are the dimensions of the image.
The original and recovered messages or watermarks can be
compared by computing the Normalized Correlation (NC)
[12]:
                        m * .m       (7)
              NC    =       *
                        m       . m


                                           *
Where m is the original message and m is the recovered
message. For unipolar vectors, m ∈ {0, 1}, and for bipolar
vectors, m ∈ {−1, 1}.
                   VII. SIMULATION RESULTS


     For all the tests in this paper, MATLAB is used. All tests
are performed upon the 8-bit grayscale 256 × 256 cameraman
image. To simulate the watermarking schemes on the
cameraman image, the four quantization levels are T0=113;
T1=124; T2=156; T3=159.
     Results of the two schemes for the cameraman image are
shown in Fig. 5 and Fig. 6, respectively. The comparison of
fidelity is shown in Table I. The numerical evaluation metrics
for all schemes in the absence and presence of attacks are
tabulated in Tables II. From Table II, we notice that the
proposed watermarking scheme achieves the lowest distortion
in the watermarked image in the absence of attacks we find
that the proposed using wavelet give the image with fidelity
better than the tech using DCT. From Table II it gives the
comparison between our technique using DCT and wavelets,
we notice also that a percentage of around 50% of the input
watermark bits can be extracted in the proposed scheme with
most of the attacks.
     In the case of DCT we find that we can detect watermark
at the presence of blurring, Gaussian or compression attack, in
the case of wavelet we can detect the watermark at the
presence of Gaussian, resizing, blurring or compression attack.         Figure 5. Watermarked image using proposed technique with DCT
We compare our results to daugads [8], LSB technique [9] and                               with and without attacks.
the technique in [4].
    In the case of LSB technique, we find it is difficult to
detect the watermark at the case of attacks applied to the
watermarked image.
    The technique in [4] gives better result than the existed
technique and the proposed one in the case of compression.




                                                                  245                             http://sites.google.com/site/ijcsis/
                                                                                                  ISSN 1947-5500
                                                           (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                           Vol. 9, No. 2, February 2011
                                                                          TABLE II. COMPARISON OF NC OF THE EXTRACTED
                                                                     WATERMARKS FOR OUR SCHEME FOR THE CAMERAMAN IMAGE
                                                                              AND THE OTHER EXISTING TECHNIQUES.




                                                                                          VIII. CONCLUSION

                                                                         This paper presented a blind DCT –DWT based image
                                                                     watermarking schemes. These schemes depend on the
                                                                     quantization of coefficients within certain amplitude ranges in
                                                                     a binary manner to embed meaningful information in the
                                                                     image. Experimental results have shown the superiority of the
                                                                     proposed schemes from the host image quality point of view
                                                                     and the blindness point of view.

                                                                                                References

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                                                                          Morgan Kaufmann Publisher, San Francisco, CA, USA.
                                                                                                                       nd
Figure 6. Watermarked image using the proposed DWT technique         [2] Schneier, B., ‘Applied Cryptography’, WILEY, 2 Edition.
                   with and without attacks.
                                                                     [3] Shaimaa A. El-said, Khalid F. A. Hussein, and Mohamed M.
  TABLE I. EVALUATION METRICS VALUES FOR ALL                             Fouad, “Adaptive Lossy Image Compression Technique,”
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                                                                                                 ISSN 1947-5500
                                                           (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                           Vol. 9, No. 2, February 2011
[10] P. Meerwald, Digital image watermarking in the wavelet         [11] S. Voloshynovskiy, S. Pereira, V. Iquise, and T. Pun. “Attack
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