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. UC Lab Kyung Hee University, South Korea January 09 , 2015
- 2. UC Lab Kyung Hee University, South Korea 2
- 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. 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. 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. 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. 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. 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. ∆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. 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. 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. 𝛻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. UC Lab Kyung Hee University, South Korea 13 (a) (b) (c) (d) (e) (f) (g) (h)
- 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. 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. 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. optimal channel selection • Combined key optimal threshold high quality of watermarked image is obtained robust against most types of attacks 17
- 18. UC Lab Kyung Hee University, South Korea 18
- 19. UC Lab Kyung Hee University, South Korea 19
Hinweis der Redaktion
- Most data on the Internet including images, videos have been not certified and protected to against the unauthorized copy issue. Therefore, we need a watermarking technique to assign a copyright on our images. In which a watermark is embedded into a host image and able to extract exactly in contention.
- The motivations for proposal of a watermarking method are robustness, imperceptibility and convenience. -> Explain each of them.
- Describe content on the slide
- The input of our method is a color image and a watermark is a binary image. The uniqueness is an optimum color channel selection to apply the embedding rule to achive an imperceptibility at the output. The wavelet coefficients have been modified based on the value of coefficient difference and watermark bits.
- The embedded images in transmission, storage process can be attacks by common digital signal proceses. In extraction, the watermark bits are extracted based on comparing the threshold with coefficient different value. The watermark bits after extracted will be reshuffling to obtain the original one.
- Describe content on the slide
- This slide show the result of image quality after embedding process based on CPNSR and SSIM parameter. Higher value of CPNSR is better. Different images with different structure and texture have different values of CPSNR. SSIM should reach to 1.
- Some results of watermark robustness after extraction under various attacking types : geometric, non-geometric, and lossy JPEG compression. Scaling image to double size and rescaling to original size modifies intensity slightly because interpolation process only consider 4 pixels in neighbor, therefore the pixel is affected by 4 pixels in surrounding. Embedding is implemented on LH and HL sub-band (middle frequency sub-bands including a part of low bandwidth), while Gaussian filter is low-pass filter, that means, this signal process will suppress high frequency and keep low frequency. Actually, the quality after using Gaussian filter is decreased, however, it is not enough. Cropping 25%, that mean, 25% content is removed end replace by 0-bits (black area). We can not extract correctly watermark bit in this area. When rotate the image, the alteration is increased from the center to border of image, and it affects to all pixels in and image and another reason is the operation of wavelet transform on horizontal and vertical dimension, while the effect of rotation is diagonal. With histogram equalization, some results is low depend on the contrast of images. Some image have the low contrast (small range in histogram) will be strongly affected by this attacks. The influence of Gaussian noise on the smooth image (less detail) is less than the image having more details (considering splash sample) Average filter is the opposite case of Gaussian noise when images which have more detail will be strongly modified by this attack (like as mandrill sample).
- Color images, same watermark payload Comparing with method of Niu at the same conditions, the proposed method is outperform in most of attacking types, except Rotation process. 1. Niu: Contourlet transform. Our method: Wavelet transform. 2. Niu: Embedding on Green channel. Our method: optimal channel selection (3 channels) 3. Niu: Embedding on Low frequencies component. Our method: middle frequencies -> better in imperceptibility 4. Niu: SVM in extraction. Out method: thresholding -> more computation time in training and classification in extraction with Niu method.
- Descibe content on the slide