Watermarking

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Watermarking

  1. 1. DIGITAL IMAGE WATERMARKING ALGORITHM USING HUMAN VISUAL SYSTEN ANANLYSIS IN DWT PRESENTED BYPRESENTED BY V.SUNDHARARAJV.SUNDHARARAJ M.EM.E ASSISTANT PROFESSOR/ECEASSISTANT PROFESSOR/ECE PAAVAI COLLEGE OF ENGINEERINGPAAVAI COLLEGE OF ENGINEERING
  2. 2. 2 OUTLINE • Introduction • DWT (Discrete Wavelet Transformation) • HVS ( Human Visual System ) • Proposed Scheme • Experimental results • Conclusions
  3. 3. 3 Introduction • The digital watermarking techniques can be classified into two categories: – Spatial domain • Less complex • Not more robust – Frequency domain • Complex • More robust
  4. 4. INTRODUCTION Watermark embedding: A digital watermark is a piece of information embedded into a digital image using Human visual system technique . WATERMARKING DETECTION: Information can be recovered from the watermarked image.
  5. 5. EXISTING METHOD ORIGINAL IMAGE SECRET IMAGE EMBED DING EMBED DING WATERMA RKED IMAGE WATERMA RKED IMAGE Embedding in this context means to add the information directly into the image data in such a way that it is not easily removed. Less complex Not more robust
  6. 6. PROPOSED METHOD Complex More robust
  7. 7. 7 DWT (Discrete Wavelet Transformation)1/2 LL1 HL1 LH1 HH1 Original image LL2 HL2 LH2 HH2 DWT 1-level DWT 2-levels A B C D A+B C+D A-B C-D L H A C B D L H A+B C+D C-DA-B LL HL LH HH
  8. 8. 8 DWT (Discrete Wavelet Transformation)2/2 DWT 2-levels LH1LH1 HH1HH1 HL1HL1 LL2LL2 HL2HL2 LH2LH2 HH2HH2
  9. 9. 9 HVS ( Human Visual System ) • The human eye is less sensitive to noise in – High frequency sub-bands – Brightness is high or low – Textured area and more near the edges
  10. 10. Watermark embedding algorithm )(i,j)x(m,nαw(i,j)I(i,j)I' θ l θ l θ l += Example: 206 50*1.2*0.1200 1112*0.11212 0 2 0 2 0 2 = += += ),)x(,(w),(I),(I' Step 1: Step 2: IDWT
  11. 11. Watermark detection algorithm • Watermark images is recovered following the expression, (i,j)αw (i,j)(i,j)-II' x'(m,n) θ l θ l θ l= Example: 50 1.2*0.1 200206 == - x'(m,n) Step 2: IDWT Step 1:
  12. 12. 12 Conclusions • The proposed scheme is based on HVS(Human Visual system) characteristics. • The proposed scheme has better performance in terms of robustness.
  13. 13. CONFERENCE [1]. V.sundhararaj , ”image watermarking using human visual system scheme in wavelet domain”, 2011,GOVERNMENT COLLEGE OF TECHNOLOGY,COIMBATORE. [2]. V.sundhararaj ,” image watermarking detector using Gauss hermite expansion in wavelet domain human visual system”, 2011,ANNA UNIVERSITY OF TECHNOLOGY,COIMBATORE. [3]. V.sundhararaj ,”watermarking detector using HVS analysis in wavelet domain human visual system”, 2012,jayalaksmi college Engg &tech. [4]. V.sundhararaj ,” image fusion detection in satellite image”, 2013,ncret, Gujrat.
  14. 14. REFERENCE [1]. Yaohui Dai,Chunxian wang, “ Digital watermarking Algorithm based on wavelet transform”, Control, Automation Systems Engineering (case), 2011 International Conference on IEEE. [2] M. M. Rahman, M. O. Ahmad, and M. N. S. Swamy, “Statistical detector for wavelet-based image watermarking using modified GH PDF,” in Proc. IEEE Int. Symp. Circuits and Systems, Seattle, WA, 2008, pp. 712–715. [3]. M. Mahbubur Rahman, M. Omair Ahmad, AUGUST 2009 “A New Statistical Detector for DWT-Based Additive Image Watermarking Using the Gauss Hermite Expansion”IEEE TRANSACTIONS ON IMAGE PROCESSING VOL. 18, NO. 8, AUGUST 2009.
  15. 15. 15 Watermark embedding algorithm(1/3) x0 3 x0 0 x0 2 x0 1 Image Watermark LL HL LH HH DWT 4-levels DWT 1-levels Step 1: Θ=0 Θ=1Θ=2 Θ=3 θ lI : The sub-band (θ) at resolution level (l) of image.
  16. 16. 16 Watermark embedding algorithm(2/3) • Find the Weight factors for wavelet- coefficient. (Barni et al.,2001) (i,j)Wθ l )(p*GT θ lI θ l S= LL2 HL2 LH2 HH2 0.21 0.1 0.12 1.13 0.25 0.36 0.37 1.38 1.40 1.2 1.3 1.4 1.6 1.7 2.1 2.3 Example: 1.21650%ST0 2 == )*( 0.1, 0.12, 0.21, 0.25, 0.36, 0.37, 1.13, 1.2, 1.3, 1.38, 1.4,1.41, 1.6, 1.7, 2.1, 2.3 Step 2:
  17. 17. 17 Watermark embedding algorithm(3/3) )(i,j)x(m,nαw(i,j)I(i,j)I' θ l θ l θ l += Example: 206 50*1.2*0.1200 1112*0.11212 0 2 0 2 0 2 = += += ),)x(,(w),(I),(I' Step 3: Step 4: IDWT
  18. 18. 18 Watermark extracting algorithm • Both the original and the watermark images are needed. (i,j)αw (i,j)(i,j)-II' x'(m,n) θ l θ l θ l= Example: 50 1.2*0.1 200206 == - x'(m,n) Step 2: IDWT Step 1:
  19. 19. 19 Experimental results (1/3) (a) Lena 512*512 (b) Watermark 64*64 (C) Watermarked Lena PSNR=44.7 dB
  20. 20. 20 Experimental results (2/3) (a) 64 times compressed watermarked Lena (b) Extracted Watermark (d) Extracted Watermark (c) 1.37% remained watermarked Lena after cropping
  21. 21. 21 Experimental results (3/3) (b) Extracted Watermark (a) Warped watermarked Lena
  22. 22. 22 Weight factor (1/4) 63 34 49 10 31 23 14 -13 15 14 3 -12 -9 -7 -14 8 0 0 1 1 0 1 10 LL3 HL3 LH3 HH3 i j 10(0,1)I,63(0,0)I 0 3 3 3 == Example:
  23. 23. 23 Weight factor (2/4) 0.10.1*1(3,3) ==Θ 2 ),,(),,(),( ),( 2.0 jiljill jiwl Ξ⋅Λ⋅Θ = θθ               = = = = ⋅        = =Θ 3if,10.0 2if,16.0 1if,32.0 0if,00.1 otherwise,1 1if,2 ),( l l l l l θ θ The human eye is less sensitive to noise in high frequency sub-bands: Example: 2 (3,0,0)(3,0,0)(3,3) (0,0)W 0.2 3 3 Ξ⋅Λ⋅Θ =
  24. 24. 24 Weight factor (3/4) ) j , i (I L(l,i,j)Λ(l,i,j) ll             += += −− 33 3 3 22256 1 1 1   <    = otherwise, 50)if,1 .L(l,i,j L(l,i,j) -L(l,i,j) L'(l,i,j) The eye is less sensitive to noise in the those areas of the image where brightness is high or low. Example: 1.75 0.751 0.251 256 64 1 2 0 2 0 256 1 1 0031003 00 3 3 = += += +=             += += ),(I ),,L(),,Λ(
  25. 25. 25 Weight factor (4/4) 10 1033 3 3 3 0 2 0 1 0 21 0 22 Var 2216 1 ,y ,xll l k x y kklkk j x, i yI j x, i yI )j,i,l( = =−− − = =θ = = θ +               ++⋅               ++ =Ξ ∑ ∑∑∑ 717164 228.6875(56) 228.68758)14-12-37-9-141513-1410(49003 2 2 = ⋅= ⋅++++++=Ξ ),,( The is less sensitive to noise in highly texture areas but,The is less sensitive to noise in highly texture areas but, among these, more sensitive near the edges.among these, more sensitive near the edges. 1.3 2 (14.83)1.750.1 2 (717164)1.750.1 (0,0)W 0.2 3 3 = ⋅⋅ = ⋅⋅ = Example:

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