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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

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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

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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.

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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!!

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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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determined is 62.0 and ,at threshold

detection The . thresholdmasking theis

andfrequency masking theis where

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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.

21

2),()(

mfmffcfc

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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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4

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w

yxw

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yx

satsat

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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

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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.

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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

) modifiedy potentiall ( watermark the:

noise the: frame original the:

)(watermark :

) watermarkNo ( :

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*1

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W

NF

NWFRXH

NFRXH

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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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WXWXsimS

),(

)(watermark ~~~~

:

) watermarkNo ( ~~~

:*

1

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NWFRXH

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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.

)~

,(

)(watermark ~~

:

) watermarkNo ( ~

:

0

*01

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WXsim

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kk

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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.