Watermarking GEO-Spatial Data - A Review Figure5 Minutiae Types for Human Figure  Figure 6 Minutiae Extraction Attack Methods to Watermarking There are numerous ways that watermarks in digital images may be destroyed electronically, especially for low robustness and easily detectable watermarks. Common examples are JPEG Compression to a lower quality image, adding image with Gaussian Blur, Gaussian Noise or Median Noise, Image Rotation to distort location and orientation, Image Sharpening, image processing by High Pass Filter and Image Chopping. Past researches often compare these resulting images (post attack to watermarked images) with the original or watermarked images to evaluate the effectiveness of different attack methods. There is often a trade off between robustness and attack. In general, watermarking low frequency components of the image is robust to attack, but can easily be changed and disappeared by compression. By contrast, the high frequency components are difficult to be compressed or filtered, but are easily perceived if the attacker is knowledgeable of the original data. Research Directions to Watermarking Vector Data Geospatial data are mostly vector graphics, especially for large-scale map information. Although the end output is image on the screen, it comes with a graphic engine by which the vector information such as coordinate location, magnitude and direction can be interpreted. Unlike raster images, the vector data do not have color information. Watermarking can only be in the thoughts of modifying and documenting the coordinates, magnitude and directions. Yet, these are also used the same for attack. Some geo-spatial data owners tend to insert extra but not easily noticeable information (lines, symbols etc.) on an ad-hoc basis to protect the copyright claims. By this approach, increasing robustness will be at the expense of data capacity, else a slight change from data generalization or compression can corrupt the watermarks. Hence, there is a need to investigate an appropriate systematic method to inserting or removing information to the data such as pattern recognition and data encryption. Undoubtedly, this will lead to some errors or distortions in location and shape, but the general pattern can still be preserved. Another approach is to uncover the attack types and extents but necessitates a detailed and time-consuming comparison of datasets. More difficulty will be posed on comparing data of two different formats, e.g. image and vector, analog and digital. For whatever approach, the algorithm on watermarking or on attack detection should balance the issues of robustness, imperceptibility and capacity or complexity. Acknowledgement The work described in this paper was supported by a grant from the Research Grant of the Hong Kong Polytechnic University (Project No. A-PG48). References
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