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Transcript of Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking...
![Page 1: Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking Using Perceptual Models by Mitchell D. Swanson, Bin Zhu,](https://reader036.fdocuments.us/reader036/viewer/2022083009/56649f4a5503460f94c6c001/html5/thumbnails/1.jpg)
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Multi-resolution Scene-Multi-resolution Scene-Based Video Watermarking Based Video Watermarking Using Perceptual ModelsUsing Perceptual Models
by Mitchell D. Swanson, Bin Zhu, and Ahmed H. Tewfik
from IEEE Journal on Selected Areas in Communications
Presenter : Wei-Cheng LinProject leader : B.H.Advisor : Prof. Ja-Ling Wu
![Page 2: Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking Using Perceptual Models by Mitchell D. Swanson, Bin Zhu,](https://reader036.fdocuments.us/reader036/viewer/2022083009/56649f4a5503460f94c6c001/html5/thumbnails/2.jpg)
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OutlineOutline
• Introduction• Author Representation v.s deadlock • Visual Masking• Temporal Wavelet Transform• Watermark Design• Watermark Detection• Visual and Robustness Results• Conclusion
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Introduction (1/2)
• Digital watermarking has been proposed as a means to identify the owner and distribution path of digital data.
• Some issues when applying watermark :– data redundancy between frames– identical watermark v.s statistical
invisibility
![Page 4: Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking Using Perceptual Models by Mitchell D. Swanson, Bin Zhu,](https://reader036.fdocuments.us/reader036/viewer/2022083009/56649f4a5503460f94c6c001/html5/thumbnails/4.jpg)
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Introduction (2/2)
• Major contributions of this paper :– A Perceptual-Based Video Watermarking
Procedure– A Scene-Based Multi-scale Watermark
Representation– An Author Representation Which Solves the
Deadlock Problem– A Dual Watermark
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Author Representation Author Representation and Deadlock (1/2)and Deadlock (1/2)
• The Deadlock Problem and Rightful Ownership ( See Figure )
• Dual watermarks– One watermarking procedure requires the
original data set for watermark detection while the other doesn’t.
– The second watermark is meant to protect the video that the pirate claims to be his original.
![Page 6: Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking Using Perceptual Models by Mitchell D. Swanson, Bin Zhu,](https://reader036.fdocuments.us/reader036/viewer/2022083009/56649f4a5503460f94c6c001/html5/thumbnails/6.jpg)
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Author Representation Author Representation and Deadlock (2/2)and Deadlock (2/2)
• Using a pseudo random sequence to represent the author :– use two keys and a suitable generator, say RS
A, Rabin, Blum/Micali , etc.– one key is author dependent ; the other is com
puted from the video signal using a one-way hash function, such as RSA, MD4.
– due to the computationally infeasible and signal dependent key, the pirate is unable to fabricate a counterfeit original that generate the desired watermark!!
![Page 7: Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking Using Perceptual Models by Mitchell D. Swanson, Bin Zhu,](https://reader036.fdocuments.us/reader036/viewer/2022083009/56649f4a5503460f94c6c001/html5/thumbnails/7.jpg)
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Visual Masking (1/4)Visual Masking (1/4)
• Frequency Masking– compute the contrast threshold at certain
frequency.
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Visual Making (2/4)Visual Making (2/4)
– to find the contrast threshold at a frequency, first use DCT and then sum rule below.
– if the contrast error at f is less than c(f), the model predict the error is invisible.
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![Page 9: Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking Using Perceptual Models by Mitchell D. Swanson, Bin Zhu,](https://reader036.fdocuments.us/reader036/viewer/2022083009/56649f4a5503460f94c6c001/html5/thumbnails/9.jpg)
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Visual Masking (3/4)Visual Masking (3/4)
• Spatial Masking– based on the threshold vision model propose
d by Girod, it accurately predicts the making effects near edges and in uniform background.
– first compute the contrast saturation
tests.alpsychovisuon based thresholda is T
and y)(x,point at the certeredGaussian a is where
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Visual Masking (4/4)Visual Masking (4/4)
– compute the luminance on the retina
– then obtain the tolerable error level for the pixel (x,y) by following formula :
– w1 and w2 are based on Girod’s model. The change to pixel less than ds(x,y) introduce no perceptible distortion.
),(),(),( 2 yxdlyxwyxdc retsat
),(),(),( 1 yxdsyxwyxdlret
![Page 11: Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking Using Perceptual Models by Mitchell D. Swanson, Bin Zhu,](https://reader036.fdocuments.us/reader036/viewer/2022083009/56649f4a5503460f94c6c001/html5/thumbnails/11.jpg)
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Temporal Wavelet Temporal Wavelet Transform (1/2)Transform (1/2)
• We employ the wavelet transform along the temporal axis of the video sequence.
• After the wavelet transform, we can get the static and dynamic components (i.e. lowpass frames and highpass frames) of the original signal.
• See the figure.
![Page 12: Multi-resolution Scene-Based Video Watermarking Multi-resolution Scene- Based Video Watermarking Using Perceptual Models by Mitchell D. Swanson, Bin Zhu,](https://reader036.fdocuments.us/reader036/viewer/2022083009/56649f4a5503460f94c6c001/html5/thumbnails/12.jpg)
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Temporal Wavelet Temporal Wavelet Transform (2/2)Transform (2/2)
• The watermark embedded in the static components ( lowpass frames ) exists throughout the entire video scene.
• The watermark embedded in the dynamic components ( highpass frames ) are highly localized in time and change rapidly from frame to frame.
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Watermark DesignWatermark Design
• First break the video sequence into scenes.
• Diagram of watermarking procedure
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Watermark Detection (1/3)Watermark Detection (1/3)
• Detection I - Watermark Detection with Index knowledge–
frame thefrom recovered
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noise the: frame original the:
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Watermark Detection (2/3)Watermark Detection (2/3)
– scalar similarity
– the overall similarity is computed as the mean of Sk for all k. and compared with the threshold to determine to presence.
– If the length of test video is the same as the original, perform the test in wavelet domain.
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Watermark Detection (3/3)Watermark Detection (3/3)
• Detection II - Watermark Detection without Index Knowledge– The hypothesis test is formed by removing
the low temporal wavelet frame from the test frame and the computing the similarity with the watermark for it.
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Visual and Robustness Visual and Robustness Results (1/2)Results (1/2)
• Visual Results ( See the figure and table )
• Robustness Results– detect the watermark when one exists and
reject a video when none exists.– for a given distortion, the overall
performance may be ascertained by the relative difference between the similarity when one present and none present.
– use the first 32 frames for test of both detection methods.
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Visual and Robustness Visual and Robustness Results (2/2)Results (2/2)
• Attacks– Colored Noise– Coding ( MPEG CR 100:1 )– Multiple Watermarks– Frame Averaging– Printing and Scanning
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Conclusion (1/2)Conclusion (1/2)
• The watermarking technique directly exploits the masking phenomena of HVS to guarantee the invisibility.
• The pseudo random sequence is generated by two keys ; one is author dependent and the other is signal dependent.
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Conclusion (2/2)Conclusion (2/2)
• Wavelet-based watermark exists at multiple scales in the video.
• The watermark can be detected with and without the index knowledge in the distortions.