Digital Image Watermarking Adam by hcj

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									Adam Day
   Applications
   Classification
   Common watermarking methods
   Types of verification/detection
   Implementing watermarking using wavelets
   Copyright Protection
    ◦ Invisibly mark products
   Manage distribution of assets
    ◦ Apply unique watermark key to each copy of a
      distributed video/image
   Embed all necessary data in a single image
   Naturally expands to video watermarking
   Simple
    ◦ Spatial Domain – Modification made to the luminance
      values
   Transformed Domain
    ◦ DCT
    ◦ DWT
    ◦ SVD
        Product of 3 matrices A = UΣVT
        U ,V are orthogonal matrices: UTU= I, VTV = I
        Σ = diag (λ1, λ2, ...).
        The diagonals of Σ are called the singular values of A
        The columns of U are called the left singular vectors of A and
        The columns of V are called the right singular vectors of A.
   An effective watermark should be:
    ◦ Robust to common manipulations
    ◦ Unobtrusive so that it does not affect visual quality
   Categorize based on:
    ◦   Capacity
    ◦   Complexity
    ◦   Invertibility
    ◦   Robustness
    ◦   Security
    ◦   Transparency
    ◦   Verification
   Fragile
    ◦ Detection fails with even minor modification
    ◦ Useful in tampering detection
    ◦ Common in simple additive watermarking
   Robust
    ◦ Detection is accurate even under modification
    ◦ Need for robustness dependent on use of data
   Non-blind
    ◦ The watermarking scheme requires the use of the
      original image
   Semi-Blind
    ◦ The watermarking scheme requires the watermark
      data and/or the parameters used to embed the data
   Blind
    ◦ If the watermarking scheme does not require the
      original image or any other data
   The 2D-DWT Transform divides the image
    into 4 sub-bands
    ◦   LL – Lower resolution version of image
    ◦   LH – Horizontal edge data
    ◦   HL – Vertical edge data
    ◦   HH – Diagonal edge data
   Most DWT watermarking algorithms embed
    only in the HL, LH and HH sub-bands

                                                 LL   HL

                                                 LH   HH
◦ Perform 2D-DWT to divide image into LL, HL, LH
  and HH sub-bands.
◦ Select coefficients from the LL, HL, LH and HH sub-
  bands that surpass a particular threshold T1
◦ Embed watermarking data via additive modification
  t’i = ti + α|ti|xi
 xi = watermark
 α = weighting constant

◦ Perform 2D-IDWT to create “watermarked image”
                                    Difference




   Modifications to edge data
    create the least visually
    perceptible changes
   If using a hard threshold to
    select coefficients, the
    number of affected              Difference




    coefficients can vary greatly
   Images with a greater
    number of edges will hold
    more watermarking data
           Watermarked
Original




Original   Watermarked
   Method
    ◦ Perform 2D-DWT to divide image into LL, HL, LH
      and HH sub-bands.
    ◦ Select coefficients from each sub-band that surpass
      a threshold T2>T1.
    ◦ Compute the correlation z, between the coefficients
      of the received image (ti*) > T2 and a particular
      watermark (yi ).
   Compute the threshold Tz.
   Detection Occurs when z>Tz.
   Comparison versus other incorrect
    watermarks show that the correct watermark
    is the only one that surpasses the threshold
                20

                15



    Threshold
                10

                5

                0

                -5
                     0   50    100    150   200   250


                              Watermarks
   DWT Watermarking schemes work well
    against most forms of image modification
    ◦   Jpeg Compression
    ◦   Downsampling -> Upsampling
    ◦   Gaussian Noise
    ◦   Median Filtering
   Technique does not work well in cases of
    image rotation
   Dependent on pixel location
                    Watermarked - Med Filt Applied                                         Watermarked                                                  Watermarked




           Median Filter Applied - T1 = 15, alpha = 0.4                 Horizontally Flipped Image - T1 = 15, alpha = 0.4                Gaussian Noise Applied - T1 = 15, alpha = 0.4
80                                                                6                                                               60
60
                                                                  4                                                               40
40
                                                                  2                                                               20
20

                                                                  0                                                                0
 0


-20                                                               -2                                                              -20
      0   50            100              150          200   250     0        50         100              150    200         250      0      50         100            150     200        250
   DWT-Based watermarking methods are fast
    /robust and protect against most forms of
    manipulation
   Schemes based on pixel dependency are
    robust in most forms of image manipulation,
    but fail when significant pixels are moved
    from their original location

								
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