1. Multi-resolution Scene-Based Video Watermarking Using Perceptual Models
by Mitchell D. Swanson, Bin Zhu, and Ahmed H. Tewfik
from IEEE Journal on Selected Areas in Communications
Presenter : Wei-Cheng Lin
Project leader : B.H.
Advisor : Prof. Ja-Ling Wu

2. Outline Introduction Author Representation v.s deadlock Visual Masking
Temporal Wavelet Transform
Watermark Design
Watermark Detection
Visual and Robustness Results
Conclusion

3. 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

4. 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

5. Author Representation 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.

6. Author Representation and Deadlock (2/2)
Using a pseudo random sequence to represent the author :
use two keys and a suitable generator, say RSA, Rabin, Blum/Micali , etc.
one key is author dependent ; the other is computed 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!!

7. Visual Masking (1/4) Frequency Masking
compute the contrast threshold at certain frequency.

8. 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.

9. Visual Masking (3/4) Spatial Masking
based on the threshold vision model proposed by Girod, it accurately predicts the making effects near edges and in uniform background.
first compute the contrast saturation

10. 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.

11. Temporal Wavelet 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.

12. Temporal Wavelet 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.

13. Watermark Design First break the video sequence into scenes.
Diagram of watermarking procedure

14. Watermark Detection (1/3)
Detection I - Watermark Detection with Index knowledge

15. 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.

16. 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.

17. Visual and Robustness 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.

18. Visual and Robustness Results (2/2)
Attacks
Colored Noise
Coding ( MPEG CR 100:1 )
Multiple Watermarks
Frame Averaging
Printing and Scanning

19. 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.

20. 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.