Digital Watermarking - PowerPoint PPT Presentation

Loading...

PPT – Digital Watermarking PowerPoint presentation | free to view - id: ce599-ZDc1Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Digital Watermarking

Description:

Error Concealment in DCT Domain. Existing algorithm embeds ROI into ROB in ... Improvement in error concealment and PAPR with redundancy in MC-CDMA systems ... – PowerPoint PPT presentation

Number of Views:347
Avg rating:3.0/5.0
Slides: 57
Provided by: jlak3
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Digital Watermarking


1
Digital Watermarking
  • Annual Progress Seminar III
  • Jayalakshmi. M
  • Roll NO. 03407003
  • Guided by
  • Prof. S. N. Merchant

2
Previous work (Annual Progress Seminar 12)
  • Significant pixels in wavelet domain for robust
    watermarking
  • Quantization of significant pixels with respect
    to HVS model
  • Watermarking in Contourlet Domain - Non-blind
    methods
  • Found suitable for images like maps which contain
    lot of lines, texts and curves
  • Error Concealment using Watermarking (DCT and DWT
    domain)

3
Overview
  • Wavelet Domain Watermarking
  • - Significant pixels with quantization given
    by HVS model with a single copy of the watermark
  • - Chair -Varshney rule for improved
    detection when multiple copies are inserted
  • Watermarking in Contourlet Domain
  • -Non-blind techniques
  • -Application mainly in geographical maps
  • -Blind watermarking in contourlet domain -
    improved detection after filtering
  • Error Concealment by Watermarking
    - Improvement in DCT domain
  • - Wavelet domain

4
1. Significant Pixel Watermarking in Wavelet
Domain
  • Significance factor for each pixel
  • Quantization of highest significant pixels with
    respect to HVS model -gives maximum allowable
    quantization for every pixel at any resolution
  • No pixel in the original image carries more than
    one watermark bit

5
Result 16x16 binary watermark

Watermarked
Original
6
Results of wavelet domain significant pixel
watermarking
  • PSNR for invisibility
  • Correlation coefficient after attacks like
  • -mean filtering, Gaussian noise addition,
  • salt pepper noise addition with median
    filtering, quantization ,JPEG compression
    cropping
  • Cases considered- 2,3 4 level decomposed image
    with and without significant pixels.
  • Performance evaluated with 16x16, 32x32 binary
    watermark and pseudorandom watermark

7
Results of wavelet domain significant pixel
watermarking
  • Retrieval with significant pixels better than
    the highest absolute pixels
  • Significance factor becomes meaningful as moved
    towards higher decomposition levels
  • The distortion to the image should be kept to
    minimum- 3rd decomposed level was found a good
    choice for embedding
  • Since a single copy of watermark is embedded,
    cropping any part of visual importance will not
    result in detection

8
Chair- Varshney rule with multiple copies of
watermark
  • Multiple copies of watermark (K)

9
Pd and Pm for each bit
  • Both Pd and Pm are fixed for all the bits in
    every copy of the watermark
  • Then, these values are increased or decreased
    depending on whether a correct detection has
    taken place or not
  • This could give rise to false positives

10
Results with CV rule
  • Correlation coefficients after JPEG compression

11
Results with CV rule
  • JPEG compressed image (Q3) and retrieved
    watermarks

CV rule
Majority rule
12
Results with CV rule
  • Correlation coefficients after mean filter and
    mean filter with JPEG compression (Q10)

13
Results with CV rule
  • Mean filtered and compressed image (Q10) and
    retrieved watermarks

CV rule
Majority rule
14
Results with CV rule
  • Correlation coefficients after histogram
    equalization

15
Results with CV rule
  • Histogram equalized and retrieved watermarks

CV rule
Majority rule
16
Results with CV rule
  • Correlation coefficients after Gaussian blur

17
Results with CV rule
  • Gaussian blurred image and retrieved watermarks

CV rule
Majority rule
18
Results with CV rule
  • Correlation coefficients after pixelization

19
Results with CV rule
  • Pixelized image and retrieved watermarks

CV rule
Majority rule
20
False Positives with Random Watermarks
  • 200 random samples of watermark

21
2. Contourlet Domain Methods
  • Sparse representation of 2-D piecewise smooth
    signals
  • 2-D wavelets formed by tensor product of 1-D
    wavelets are good at catching discontinuities at
    edge points
  • Wavelets do not see smoothness along the contours
  • Contourlets make use of Pyramidal Directional
    Filter Bank (PDFB)
  • PDFBLPDFB

22
Directional decomposition used in proposed methods
23
Method 1- High absolute coefficients
  • Directional decomposition doubles at every scale
  • Highest absolute coefficients in D is watermarked
  • DDa m

24
Method 2 (Significance factor)
25
Generalized Neighborhood
26
Watermarked Images
Wavelet based
27
Watermarked Images
Highest absolute pixel (Multiple6)
Significant pixel (Multiple3)
28
Watermarked Images
Gen. neighborhood
Highest abs. (curve scale)
29
Watermarked Images

DCT
Hadamard
30
Retrieved watermarks after mean filtering
Wavelet (wave4)
Highest absolute pixels(multiple6)
Significant pixels(multiple3)
Gen. neighborhood(gen1)
Curve scaling relation(gen2)
DCT Hadamard
31
Conclusion Non-blind techniques in contourlet
domain
  • Contourlet based algorithms performed better than
    wavelet, DCT Hadamard transform domain methods
    in images like maps
  • Significant pixels in contourlet domain were
    found the best choice for watermark embedding for
    images containing lot of curves and texts
  • l

32
Blind Watermarking in Contourlet Domain
  • Contourlet decomposition of images
  • Binary watermark embedded using spread spectrum
    method
  • Additive embedding is performed
  • Robustness verification is done using Stir Mark
    attack
  • Recovered watermark is evaluated by finding its
    correlation with original logo
  • The visual similarity is improved by median
    filtering of the retrieved logo

33
Additive Embedding
  • Y Set of original pixels
  • Y Corresponding watermarked pixels
  • PPseudorandom sequence generated using a key
  • w watermark bit

Original image
Water- marked image
Pseudo random sequence
key
34
Embedding- Results
  • Original image Logo
    Watermarked image

35
Embedding- Results
  • PSNR with embedding

36
Retrieved logo
  • Retrieved logo with authorized key
  • Retrieved logo with unauthorized key
  • Retrieved logo after post processing

37
Correlation coefficients with a
  • Retrieval results

38
Correlation coefficients after attacks
39
Mean filtering (256x256, a0.225)
  • Mean filtered image Retrieved logo
    After post-processing

g0.7077 g 0.8704
40
Quantization to multiples of 50 (256x256,
a0.225)
  • Quantized image Retrieved logo
    After post-processing

g0.7914 g 0.9301
41
Quantization to multiples of 100 (256x256,
a0.225)
  • Quantized image Retrieved
    logo After post-processing

g0.6808 g 0.8332
42
JPEG Compression (Q40) (256x256, a0.225)
  • Quantized image Retrieved
    logo After post-processing

g0.6939 g 0.8419
43
Watermarked Image with 128x384 Coefficients
(a0.275)
  • Watermarked Retrieved logo
    After post-processing

g0.8021 g0.9438
g0.8322 g0.9735
44
Mean filtering with 128x384 Coefficients
Watermarked ( a0.275)
  • Mean filtered image Retrieved
    logo After post-processing

g0.7280 g 0.8984
45
Mean filtering with 128x384 Wavelet Coefficients
Watermarked (a0.275)
  • Mean filtered image Retrieved logo
    After post-processing

g0.6682 g 0.8341
46
Conclusion blind watermarking in contourlet
domain
  • Blind watermarking in contourlet decomposed
    images is performed using spread spectrum
    technique
  • Correlation based detection is improved using
    post processing after detecting all the bits
  • Shows good robustness against attacks
  • Directional bands can be effectively used to
    achieve robustness against geometrical attacks

47
3. Error Concealment Using Watermarking
  • Watermark derived from image itself

48
Algorithm
  • Approximate band embedded in LH1 HL1
  • N x N approximate band represented by 8N2 bits
  • LH1 HL1 together gives 32N2 bits
  • 4 copies of watermark hidden with a shift in the
    bit stream
  • Sign of every pixel remains unchanged after
    embedding
  • Completely blind algorithm
  • Results compared with BNM technique

49
Results of EC in DWT domain -8X80 block errors
  • 20 line errors

PSNR18.94 PSNR
33.4 PSNR 32.58
Error image Proposed
method BNM
50
Error Concealment in DCT Domain
  • Existing algorithm embeds ROI into ROB in spatial
    domain
  • ROI embedded into the compressed JPEG bit streams
  • Good resistance to compression
  • 32x32 ROI into the JPEG quantized pixels through
    LSB substitution
  • Only 7th to 16th pixels in the zigzag scanned
    pattern of each 8x8 block in textured regions are
    selected for hiding the watermark bits.

51
Results with medical image
  • ROI defined on the fracture

52
Results with only ROI embedded(after compression)
  • ROI Spatial
    Proposed

53
Results with only ROI embedded(after compression)
  • ROI Spatial
    Proposed

54
Results with only ROI embedded(after compression)
  • PSNR of error concealed ROI

55
Error Concealment through watermarking
  • DWT domain algorithm has two levels of security
    using a scale factor and a key for pseudorandom
    generation
  • Also efficiently conceals error in any part of
    the image
  • DCT based method needs lot of texture region in
    the ROB for hiding the watermark and ROI defined
    is small compared to the size of the image

56
Future work
  • Improvement in error concealment and PAPR with
    redundancy in MC-CDMA systems
  • Effect of super resolution on watermarking

57
Thank you
About PowerShow.com