International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004
Table 1. Comparison of Experimental Results
Testing Type Testing COX method DCT Entire algorithm Our algorithm
: Number
Filter JMedian, Gauss, FMLR, Sharpening! 5 5 5
JPEG compression 10 10 10 10
Symmetrically and non-symmetrically 5 5 5 5
moving rows and lines
Current linear geometrical : 5 3 5 5
transform 10 10 10 10
Changing x-y axes display scale > > 5 5
Scale transform 5 3 5
Cropping in x-y directions 15 8 10 13
Circumvolving with cropping and no scale
transform 15 7 9 10
Circumvolving with cropping and scale
transform 10 2 3 8
Center cropping 2 2 2 A
StirMark random bend
Summation 87 59 69 78
6. Conclusions
In this paper we proposed CBWM localized adaptive
watermarking algorithm base on DWT. In this algorithm, we
firstly extracted edges and chose feature points in the original
remote sensing image, then established each sub-image, in the
center of which is the feature point, and embedded the local
watermarks into the feature vectors in the three-level DWT
low-frequency space (LL3) of sub-images and the local
watermark is orthogonal to the feature vector,
watermark is sub-image content-based. In addition the
embedding procedure was adaptive, namely we firstly
organized wavelet coefficients into wavelet blocks, classified
those wavelet blocks by texture intension and according to the
classification results, adaptively embedded watermark weights
of different strength into different weights of the feature vector.
Furthermore the algorithm exploited the Neymann-Pearson
criterion to detect local watermarks. And we exploited StirMark
as the testing instrument to verify robustness of the algorithm.
And experiments testified that the algorithm has a strong
robustness, simultaneously a good resistive ability against filter,
noise, geometrical transform, remote sensing image
compression, x-y direction cropping and StirMark attack and
has a certain robustness against remote sensing image rotation,
which needs to improve further. In addition, after embedding
watermarks, there is little influence on such applications of the
remote sensing image as edge detection and image classification.
Therefore the watermarking algorithm is a practical one for
remote sensing images.
Moreover, we could improve robustness of CBWM localized
adaptive watermarking algorithm based on DWT for remote
sensing images from the following 2 aspects:
1) theoretically as long as part of the remote sensing image
after cropping contains at least one feature point and the
corresponding sub-image, the watermarks can be detected. But
after compression and other image processing (such as filter,
noise adding), the feature points in the remote sensing image
can't all be detected, which would lead to failure of
namely .
196
watermarking detection. So when detecting watermarks, we
should detect not only from the sub-image of the feature point,
but also from all candidate points and the points in the near
areas around them. As long as in one sub-image corresponding
to some point, the watermark can be detected, we think there be
localized watermarks in the image. But that would increase
complexity of the computation;
2) the algorithm is more complicated in computation, so we can
consider making heuristic search by heuristic knowledge to
decrease the computation during the course of optimization
computation.
In addition, we can consider to improve robustness of the
algorithm from 8 aspects:
1 )making feature points possess stronger stability;
2)exploiting the algorithm which is more suitable to embed
local watermarks in remote sensing sub-images, consequently
improves robustness of local watermarks;
3)choosing more appropriate size of local area to embed
watermarks, therefore further increasing the ability to resist
cropping and StirMark attack and the robustness of local
watermarks;
4)choosing more suitable scale gene ‘a’ to better control
embedding intensity and energy of local watermarks;
5)classification of wavelet blocks can also be done in the spacial
area (Huang Daren, 2002a). Each wavelet block all corresponds
with a sub-image of the same size in the spacial area and those
sub-images are not overlapped and formed a kind of division of
the original remote sensing image. In addition edge points
represent those breaking points of image pixel grays, therefore
if a sub-image has more edge points, it would have stronger
texture; and so the texture of the wavelet block corresponding
with the sub-image would be stronger. Therefore we can
consider to classify the wavelet blocks by exploiting the density
of edge points;
6)managing to make watermarks resist cropping as well as
zoom and general affine transform;
7)developing the digital watermarking technology based on
features (Yi Kaixiang, 2001a). Watermarking technology based