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

Outline Introduction Embedding watermark Extraction and pattern recovery Results Conclusions

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

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

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

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

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

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

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)

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

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.

Results

Results

Results

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