Digital Watermarking Digital Watermarking With Phase Dispersion With Phase Dispersion Algorithm Algorithm Team 1 Final Presentation Team 1 Final Presentation SIMG 786 SIMG 786 Advanced Digital Image Advanced Digital Image Processing Processing Mahdi Nezamabadi, Mahdi Nezamabadi, Chengmeng Liu, Chengmeng Liu, Michael Su Michael Su
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Digital Watermarking With Phase Dispersion Algorithm Team 1 Final Presentation SIMG 786 Advanced Digital Image Processing Mahdi Nezamabadi, Chengmeng Liu,
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Digital Watermarking With Digital Watermarking With Phase Dispersion AlgorithmPhase Dispersion Algorithm
Team 1 Final PresentationTeam 1 Final Presentation
SIMG 786 SIMG 786
Advanced Digital Image ProcessingAdvanced Digital Image Processing
Embedding process illustrationEmbedding process illustration
Watermark extraction processWatermark extraction process
Indices for image differenceIndices for image difference
• MSE (Mean square error)
2
1 1
1
N
i
M
j
i,jIi,jI'NM
I,I'MSE
• Correlation factor
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),(),('
yxMyxM
yxMyxMCF
Similarity vs. α
• Similarity is measured by cross correlation between original and extracted log
• 64 tiles were used in embedding
• The α controls the visibility of the watermark logo in the watermarked image
• The α also depends on the number of tiles
Implementation of Binary Message Implementation of Binary Message template function 1template function 1
• embedding binary information consists of representing the one and zero bits by positive delta function and black that are placed in predefined and unique locations within the message image.
• It consisted of concentric circles with equal increments in radius and random angular displacement.
• A 64 bits template is shown on left
• The error rate is 0 for this 64 bits template
Implementation of Binary Message Implementation of Binary Message template function 2template function 2
• 650 bits template function is shown on the left
• 650 bits can embed 32 characters by repeating them 5 times with no compression
• The error rate is 0.46% for this 650 bits template, that means the probability for get a wrong bit is 9.7e-8
Rotation/Scale DetectionThresholding
Rotation/Scale DetectionImage rotation
Rotation/Scale DetectionImage rotation
Robustness to lossy compressionRobustness to lossy compression
Original size Resolution MSE Correlation Factor
4.1MB 2k X 2k pixels 0.1194 0.5130
Compressed size
Compression ratio
555KB 7 0.1385 0.4172
312KB 13 0.1562 0.3798
199KB 20 0.1901 0.3251
Attacked by low pass filter
The watermarked image is blurred The extracted logo is equivalent to original log convolve with a low pass filter
Robustness with noiseRobustness with noise
Multiple watermarksMultiple watermarks
With the same keyEmbedded and extracted with different keys
Robustness to CroppingRobustness to Cropping
Halftoning can destroy the Halftoning can destroy the correlation between image and correlation between image and
watermarkwatermarkLena after printed and scanned Extracted watermark
ConclusionsConclusions
• This algorithm works best under the following circumstances:
• α = 0.2 gives the best balance between visibility and signal strength.
• Bigger image size and smaller watermarks ( more tiles).• Bigger color depths.• The algorithm can resist the following attacks: lowpass