1. Source: Pattern Recognition Letters, Article In Press, 2007
Speech authentication system using digital watermarking and pattern recovery
Author: Chang-Mok Park, Devinder Thapa, Gi-Nam Wang, AJOU University, South Korea
Source: Pattern Recognition Letters, Article In Press, 2007
Speaker: Yu-Che Hsieh

2. Outline Introduction Embedding watermark
Extraction and pattern recovery
Results
Conclusions

3. Introduction Proposed approach Record device Three type Audio data
Two process
Avoid re-embedding
Cross-correlation
Cyclic pattern
Audio data
Store format
MP3,WMA,…,etc

4. Embedding watermark
Spread spectrum in DFT (discrete Fourier transform) domain
Magnitude vector: y = m + w
w = △b
b: Random bit sequence b (i), where i = 1, 2,…,N
△: Embedding strength
Embedding schema

5. Embedding watermark Structure of a watermark pattern Cyclic pattern
Watermark information(0,1,2,3 and 4) is fromed by shifting the random bit sequence
Tradeoff
Robustness and precision
Between length and information

6. Embedding watermark In this paper: Length of T: 0.6s
Watermark information:
0, 1, 2, 3, 4

7. Extraction and pattern recovery
Detection of watermark information using cross-correlation
cross-correlation function
A larger correlation value must be expected in the specific position
Speech signal
DCT process

8. Extraction and pattern recovery
Peak ranging from 0 to N – 1
Watermark information: Index of peak
Watermark information can be extracted using the index of the peak

9. Extraction and pattern recovery
Extracting the temporal pattern: two preprocessing steps
Estimation with near right information:
v (t) should be 0 ~ 4
Curve optimization (smoothing)

10. Extraction and pattern recovery
Extracted pattern and recovered pattern
Dash line: Original pattern
Dot line: smoothed pattern

11. Results Normal recovered pattern and some modified patterns
Normal Pattern
(1) Type-I, Insertion of non-watermarked speech signals.
(2) Type-II, Insertion of watermarked speech signals.
(3) Type-III, Removal of speech signals.

12. Results

13. Results

14. Results

15. Conclusions Speech authentication approach Cyclic pattern embedding
Pattern recovery
Preprocessing and curve fitting
Detect the various forgeries