The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008
Figure 4. The flow of detecting watermark
Where lfl{k) represents the original watermark,
rn\k) represents the detected watermark, VI represents the
length of bit for watermark.
Here the original data of coordinate points are needed to record
so as to detect the watermark.
5 THE EXPERIMENTS
We now present the experimental results for the proposed
watermarking algorithm. The watermark is shown in Figure 1,
and the original data to embed watermark is the contour data
including 160182 coordinate points with the scale 1:250000.
5.1 Visibility
Figure 5 shows the embedded watermark data overlapped with
the original data, where the solid lines is the original contour
data and the hidden lines is the embedded watermark data. From
the comparison of the two kinds of data in Figure 5, it can be
known that the proposed watermarking algorithm is with good
imperceptibility.
Figure 5 the imperceptibility for the embedded watermark data
5.2 The precision analysis
There are 160182 coordinate points in the original and
embedded watermark data. We compare the absolute error
between two kinds of data. The comparison results list is in
Table 1.
Absolute error C
C=0
C=1
C=2
02
Number of points
155024
4204
954
0
Percent
96.78%
2.62%
0.60%
0
Table 1 The error between original and embedded watermark
data
From Table 1, it can be known that there is no error for the
96.78% of total data. For the points with the error, the error is
allowable (<=2), and the quality of data with watermark can
satisfy the actual applications. Hence the proposed
watermarking algorithm is with high precision.
5.3 The robustness
In the subsection, the robustness for the embedded watermark
data is studies, and the watermark with intensity 1 and max
error 2 is detected after meeting the different attack.
(1) No change
If there is no any change for the embedded watermark data, the
watermark can be detected wholly.
(2) Compressing
If compressing the embedded watermark data, the watermark
also can be detected and the result is shown in Figure 6, where
the correlative coefficient is 0.86689.
Figure 6. The detected watermark by compressing
(3) Deleting points
We delete some points randomly from the embedded watermark
data, and then detect the watermark from the data. The result is
shown in Figure 7, where the percent of deletion is 10% and the
correlative coefficient is 0.904580.
Figure 7. The detected watermark by deleting points
(4) Noise attacking
If attacking the embedded watermark data by uniform noise, the
watermark also can be detected and the result is shown in
Figure 8, where the correlative coefficient is 0.857824.