Bit-Plane Watermarking for SPIHT-Coded Images 台北科技大學資工所 指導教授：楊士萱 學生：廖武傑 2003/07/29

OUTLINE Introduction to watermarking SPIHT Factors that affect watermarking system Proposed watermarking scheme Conclusion

Watermarking The watermark is an imperceptible but indelible code used for ownership identification. Why? convenience of digital multimedia data intellectual property of digitally recorded material shortcomings of encryption techniques Requirements perceptual transparency robustness security appropriate complexity Computational requirements introduced by watermark embedding and detection should be small

Domains of image watermarking

OUTLINE Introduction to watermarking SPIHT Factors that affect watermarking system Proposed watermarking scheme Conclusion

SPIHT coding procedure

Wavelet transform(1D) h0 h1 h0 h Resolution 1/2 Resolution 1/4 C 3,n C 2,n C 1,n d 2,n d 1,n

Wavelet transform(2D) h0 h Horizontal filteringVertical filtering

Wavelet filter Daubechies 9/7 Analysis Filter Coefficients: iLow-Pass FilterHigh-Pass Filter ± ± ± ±

Example, “Lena”

SPIHT coding procedure

Spatial Orientation Trees

Example (1 st Sorting pass, Encoder) LIPLSP LIS: T=32 A2A3A

Example (1 st Sorting pass) LIPLSP LIS: T=32 A3A B

Example (1 st Sorting pass) LIPLSP LIS: T=32 A B B3 001

Example (1 st Sorting pass) LIPLSP LIS: T=32 A B A9 001 A10A11A

Example (1 st Sorting pass) LIPLSP LIS: T=32 A B A9 001 A11A

Example (1 st Refinement pass) Do nothing

Example (2nd Sorting pass) LIPLSP LIS: T=16 A4B2A9A11A

Example (2nd Refinement pass) LSP: In 1st sorting pass:

Example (3rd Sorting pass) LIPLSPT=8

Example (4th Sorting pass) LIPLSPT=4

Example (5th Sorting pass) LIPLSPT=2

Example (1 st Sorting pass, Decoder) Received: originaldecoded

Example (1 st Sorting pass, Decoder) Original coefficients Reconstructed value (by 1st sorting pass) 48

Example (2 nd Sorting pass, Decoder) Received: originaldecoded

Example (2 nd Refinement pass, Decoder) Original coefficients Reconstructed value (by 1st sorting pass) Refinement bit: 56 Refinement by 2nd refinement pass 40 If refinement bit is ‘0’

Example (2 nd Refinement pass, Decoder) Received: originaldecoded 1010

OUTLINE Introduction to watermarking SPIHT Factors that affect watermarking system Proposed watermarking scheme Conclusion

Watermarks over a noise channel The noise comes from:  The host signal  Lossy compression  Other attacks

Host-interference-nonrejecting watermarking method Transform domain watermarking Watermark: Gaussian random source Embedding method: After distortion: Effect of host interference and quantization noise W. Zhu, Z. Xiong, and Y.-Q. Zhang, “Mutiresolution watermarking for images and video,” IEEE Trans. Circuits Syst. Video Technol., June 1999

Watermark similarity measure for judging: (linear correlation) where: Informed detection: Effect of host interference and quantization noise (cont.)

Test images “Lena”“Baboon”

Degradation of watermark similarity due to quantization Bpp1/641/321/161/81/41/21 Step size MSE PSNR Correlati on (informe d) Correlati on (blind) “Lena”, alpha=0.26, watermark length:255, subband:1-6, coefficients smaller than 512

Degradation of watermark similarity due to quantization “Baboon”, alpha=0.28, watermark length:255, subband:1-6, coefficients smaller than 512 Bpp1/641/321/161/81/41/21 Step size MSE PSNR Correlati on (infor med) Correlati on (blin d)

OUTLINE Introduction to watermarking SPIHT Factors that affect watermarking system Proposed watermarking scheme Conclusion

Proposed watermarking scheme

Proposed watermarking scheme (Cont.) Embedding our watermark

The effect of wavelet coefficient after embedding watermark

Extracting watermark procedure

Watermarking detecting

Proposed watermarking scheme (Cont.) Host-interference rejection Minimization of quantization effects Low complexity Progressive nature

StirMark attacks JPEG compression Gaussian filtering Median filtering Sharpening FMLR

JPEG compression attacks

Gaussian filtering

Median filtering

Sharpening

FMLR

watermark length:255 PN code, subband:1-6, coefficient between 512 and 256 soft-decision, bit-rate: 0.25bpp LenaBaboonPepperF16 Jpeg 50% PSNR / / / / / / / Jpeg 30% PSNR / / / / / / / / Jpeg 10% PSNR / / / / / / / / Gaussian filter PSNR / / / / / / / / Median filter PSNR / / / / / / / / Sharpening PSNR / / / / / / / / FMLR PSNR / / / / / / / /

watermark length:255 PN code, subband:1-6, coefficient between 512 and 256, soft-decision, bit-rate: 0.5bpp LenaBaboonPepperF16 Jpeg 50% PSNR / / / / / / / / Jpeg 30% PSNR / / / / / / / / Jpeg 10% PSNR / / / / / / / / Gaussian filter PSNR / / / / / / / / Median filter PSNR / / / / / / / / Sharpening PSNR / / / / / / / / FMLR PSNR / / / / / / / /

Enhanced watermarking by repetition codes

Performance (basic vs. enhanced) By soft-decision:  The difference between basic and enhanced scheme is small. By hard-decision:  Enhanced scheme is superior to basic scheme. (except some particular attacks)

OUTLINE Introduction to watermarking SPIHT Factors that affect watermarking system Proposed watermarking scheme Conclusion

Our watermarking system suffers less from the host interference and quantization noise. The embedding and extracting of watermarks are simple. The performance can be improved by enhanced scheme with larger noise margins.