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UC Lab Kyung Hee University, South Korea
January 09 , 2015
UC Lab Kyung Hee University, South Korea 2
UC Lab Kyung Hee University, South Korea 3
• Millions of images are shared every day through the SNS
• Many of these image...
UC Lab Kyung Hee University, South Korea 4
Convenience
Imperceptibility
Robustness
The information should be extracted fro...
visualization
frequency domain
• Quality
 Optimal color channel selection
• Accuracy rate in the extraction process
 Opt...
UC Lab Kyung Hee University, South Korea 6
Article Key concept Limitations
Xiang-yang [2012] Fourier transform
Least squar...
UC Lab Kyung Hee University, South Korea 7
Color
Images
4-DWT
Coeffient
Blocking
Coeffient
Difference
Optimum
Selection
Em...
UC Lab Kyung Hee University, South Korea 8
Color
Images
4-DWT
Coefficient
Blocking
Coefficient
Difference
Optimum
Selectio...
∆𝑖,𝑘 (= 𝐶𝐿𝐻𝑖,𝑘
− 𝐶 𝐻𝐿 𝑖,𝑘
)
∆𝑖,𝑘→ ∆𝑖,𝑘
𝑆
𝛻𝑖
0
= 𝑦1 − ∆𝑖,𝑘
𝑆
𝑦1 =
1
𝑁0
𝑘=1
3
𝑖=1
𝑁0
∆𝑖,𝑘
𝑆
𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡
= 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡
+ 𝛻𝑖
0...
The embedding process
UC Lab Kyung Hee University, South Korea 10
∆1,𝑅 ∆1,𝐺 ∆1,𝐵
∆2,𝑅 ∆2,𝐺 ∆2,𝐵
⋮ ⋮ ⋮
∆ 𝑛−1,𝑅 ∆ 𝑛−1,𝐺 ∆ 𝑛−...
UC Lab Kyung Hee University, South Korea 11
The extraction process
Embedded
Image
Recovered
Watermark
Bit
Reshuffling
Extr...
𝛻𝑖
1
= 𝑦2 − ∆𝑖,𝑘
𝑆
𝑦2 =
1
𝑁0
𝑘=1
3
∆𝑖=𝜆𝑁1,𝑘
𝑆
∆𝑖,𝑘
𝑆
< 𝑦2
𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡
= 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡
+ 𝛻𝑖
1
𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡
= 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡
− 𝛻...
UC Lab Kyung Hee University, South Korea 13
(a) (b) (c) (d)
(e) (f) (g) (h)
UC Lab Kyung Hee University, South Korea 14
Table 1: Quality of embedded images
Image CPSNR (dB) SSIM
Airplane 45.81 0.992...
UC Lab Kyung Hee University, South Korea 15
Watermark robustness after extraction (NC value)
0.4
0.5
0.6
0.7
0.8
0.9
1
Non...
UC Lab Kyung Hee University, South Korea 16
0.4
0.5
0.6
0.7
0.8
0.9
1
Scaling Cropping
20%
Rotation (5) Gaussian
noise
Med...
optimal channel selection
• Combined key
optimal threshold
high quality of watermarked image is obtained
robust against mo...
UC Lab Kyung Hee University, South Korea 18
UC Lab Kyung Hee University, South Korea 19
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A Novel Watermarking Scheme for Image Authentication in Social Networks

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This paper presents a novel watermarking scheme for authentication of digital color images in social networks. The procedure consists of the embedding of a binary watermark image, containing the owner information, into the image to be authenticated. In order to minimize the artifacts in the host image the process is carried out in the wavelets domain. Concretely, the watermark embedding is performed in the HL4 and LH4 sub-band coefficients of the red, green and blue channels of the original image, based on an optimal channel selection quantization technique. To ensure a high robustness to tampering and malicious attacks a key-based pixel shuffling mechanism is further used. The reverse process is likewise identi fied for the extraction of the watermark from the authenticated image. Both embedding and extraction procedures are benchmarked on diverse color images and under the e ffects of diff erent types of attacks, including geometric, non-geometric, and JPEG compression transformations. The proposed scheme proves to support imperceptible watermarking, while also showing a high resiliency to common image processing operations.

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A Novel Watermarking Scheme for Image Authentication in Social Networks

  1. 1. UC Lab Kyung Hee University, South Korea January 09 , 2015
  2. 2. UC Lab Kyung Hee University, South Korea 2
  3. 3. UC Lab Kyung Hee University, South Korea 3 • Millions of images are shared every day through the SNS • Many of these images end up in the hands of unknown people that use them in an illegal and malicious manner • Mechanisms for ensuring the ownership and protecting the copyright are utterly required to settle potential disputes Image Watermarking
  4. 4. UC Lab Kyung Hee University, South Korea 4 Convenience Imperceptibility Robustness The information should be extracted from the original image A watermark has to be imperceptible A watermark needs to be robust against image modifications
  5. 5. visualization frequency domain • Quality  Optimal color channel selection • Accuracy rate in the extraction process  Optimal threshold based on the Otsu method. UC Lab Kyung Hee University, South Korea 5
  6. 6. UC Lab Kyung Hee University, South Korea 6 Article Key concept Limitations Xiang-yang [2012] Fourier transform Least square support vector machine (LS-SVM) High computation time for LS-SVM training. Niu [2011] Nonsubsampled coutourlet transform (NSCT) Support vector regression (SVR) Low performing NSCT and computation time in extraction process. Song [2012] Curvelet transform Coefficient quantization technique Weakness under lossy JPEG compression Chou [2010] Wavelet transform Just noticeable color difference (JNCD) Weakness under geometric operations and hue modification.
  7. 7. UC Lab Kyung Hee University, South Korea 7 Color Images 4-DWT Coeffient Blocking Coeffient Difference Optimum Selection Embedding Rule Coefficient Unblocking 4-IDWT Embedded Image Binary Watermark Bit shuffling Combined Key Recovered Watermark Bit Reshuffling Extraction Rule Coefficient Difference Coefficient Blocking 4-DWT Modified Image Adaptive threshold Otsu method Channel Attacks Embedding process Extraction process
  8. 8. UC Lab Kyung Hee University, South Korea 8 Color Images 4-DWT Coefficient Blocking Coefficient Difference Optimum Selection Embedding Rule Coefficient Unblocking 4-IDWT Embedded Image Binary Watermark Bit shuffling Combined Key HL4 (C(HL,i)) LH4 (C(LH,i)) Block 1 C(HL,1 ) Block 2 Block n C(HL,2) C(LH,1 ) C(LH,2)C(HL,n) C(HL,n) Coefficient difference Numberofblocks Before embedding The embedding process Coefficient difference Numberofblocks After embedding 0-bits 1-bits y1 y2
  9. 9. ∆𝑖,𝑘 (= 𝐶𝐿𝐻𝑖,𝑘 − 𝐶 𝐻𝐿 𝑖,𝑘 ) ∆𝑖,𝑘→ ∆𝑖,𝑘 𝑆 𝛻𝑖 0 = 𝑦1 − ∆𝑖,𝑘 𝑆 𝑦1 = 1 𝑁0 𝑘=1 3 𝑖=1 𝑁0 ∆𝑖,𝑘 𝑆 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 = 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 + 𝛻𝑖 0 ; ∀𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 ≥ 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 = 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 + 𝛻𝑖 0 ; ∀𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 < 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 UC Lab Kyung Hee University, South Korea 9 LL4 HL4 LH4 HH4 HL4 (C(HL,i)) LH4 (C(LH,i)) Block 1 C(HL,1 ) Block 2 Block n C(HL,2) C(LH,1 ) C(LH,2)C(HL,n) C(HL,n)
  10. 10. The embedding process UC Lab Kyung Hee University, South Korea 10 ∆1,𝑅 ∆1,𝐺 ∆1,𝐵 ∆2,𝑅 ∆2,𝐺 ∆2,𝐵 ⋮ ⋮ ⋮ ∆ 𝑛−1,𝑅 ∆ 𝑛−1,𝐺 ∆ 𝑛−1,𝐵 ∆ 𝑛,𝑅 ∆ 𝑛,𝐺 ∆ 𝑛,𝐵 ∆𝑖,𝑘 − 𝑦1 ; ∀ 0 − bit ∆𝑖,𝑘 − 𝑦2 ; ∀ 1 − bit
  11. 11. UC Lab Kyung Hee University, South Korea 11 The extraction process Embedded Image Recovered Watermark Bit Reshuffling Extraction Rule Coefficient Difference Coefficient Blocking 4-DWT Modified Image Adaptive threshold Otsu method Attacks HL4 (C(HL,i)) LH4 (C(LH,i)) Block 1 C(HL,1 ) Block 2 Block n C(HL,2) C(LH,1 ) C(LH,2)C(HL,n) C(HL,n) Coefficient difference Numberofblocks After embedding Optimal threshold 0-bits 1-bits
  12. 12. 𝛻𝑖 1 = 𝑦2 − ∆𝑖,𝑘 𝑆 𝑦2 = 1 𝑁0 𝑘=1 3 ∆𝑖=𝜆𝑁1,𝑘 𝑆 ∆𝑖,𝑘 𝑆 < 𝑦2 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 = 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 + 𝛻𝑖 1 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 = 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 − 𝛻𝑖 0 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 ≥ 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 = 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 − 𝛻𝑖 1 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 = 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 + 𝛻𝑖 0 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 < 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 ∆𝑖,𝑘 𝑆 ≥ 𝑦2 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 = 𝐶𝐿𝐻 𝑖,𝑘_𝑜𝑝𝑡 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡 = 𝐶 𝐻𝐿 𝑖,𝑘_𝑜𝑝𝑡
  13. 13. UC Lab Kyung Hee University, South Korea 13 (a) (b) (c) (d) (e) (f) (g) (h)
  14. 14. UC Lab Kyung Hee University, South Korea 14 Table 1: Quality of embedded images Image CPSNR (dB) SSIM Airplane 45.81 0.992 Girl 53.13 0.999 House 43.41 0.995 Lena 48.17 0.999 Mandrill 46.75 0.999 Peppers 44.57 0.999 Sailboat 43.73 0.998 Splash 55.28 0.999 Image quality after embedment Original Watermarked
  15. 15. UC Lab Kyung Hee University, South Korea 15 Watermark robustness after extraction (NC value) 0.4 0.5 0.6 0.7 0.8 0.9 1 Non-attack Scaling Cropping 25% Rotation (0.5) Gaussian noise Geometric attacking Airplane Girl House Lena Mandrill Peppers Sailboat Splash 0.4 0.5 0.6 0.7 0.8 0.9 1 Histogram equalization Average filter 7x7 Median filter 7x7 Gaussian filter 7x7 Non-geometric attacking Airplane Girl House Lena Mandrill Peppers Sailboat Splash 0.4 0.5 0.6 0.7 0.8 0.9 1 JPEG QF=10% JPEG QF=20% JPEG QF=40% JPEG QF=60% Lossy JPEG compression Airplane Girl House Lena Mandrill Peppers Sailboat Splash
  16. 16. UC Lab Kyung Hee University, South Korea 16 0.4 0.5 0.6 0.7 0.8 0.9 1 Scaling Cropping 20% Rotation (5) Gaussian noise Median filter 3x3 Gaussian filter 3x3 JPEG QF=30% JPEG QF=40% JPEG QF=70% Robustness comparison - NC Niu [7] Proposed 40.71 48.17 36 38 40 42 44 46 48 50 Niu's method Proposed method Imperceptibility comparison - CPSNR (dB)
  17. 17. optimal channel selection • Combined key optimal threshold high quality of watermarked image is obtained robust against most types of attacks 17
  18. 18. UC Lab Kyung Hee University, South Korea 18
  19. 19. UC Lab Kyung Hee University, South Korea 19

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