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Watermarking GEO-Spatial Data - A Review

Digital Watermarking
To hide the watermarking signal, the watermark signal has to be concealed within the original data or data sources (asset). Data concealment is the main process for watermarking. In general, it is a reverse approach for data compression. For data compression technology, its aim is to remove unnecessary information from the data sources all those data which are perceptually less important. For the data hiding technology, the goal is to add some information to the data source so that the user will not be perceptually alerted. To keep the robustness, the output of the watermarked data should be difficult to be detected or easily removed by some filtering and digital processing. If there is a big change of the source data, the end-user will be easily alerted by comparing the non-watermarked data and the watermarked data.

There are many researches on watermarking still image on the screen that follows the Human Visual System (HVS). No matter unnecessary data are removed or extra information is added, the user should not be alerted to have a big change from the original image. Human eye is not able to perceive light radiation equally at all frequencies (Figure 3), but is more sensitive to red than blue color. It is more sensitive to middle frequencies and less to low and high frequencies of the visible spectrum.


Figure 3, Human eye visible spectrum response with spectral distribution of the color

For watermarking still images, the image binary data sequence should undergo a transformation (Discrete Fourier Transform DFT or Discrete Cosine Transform DCT). The spectrum in frequency domain is analyzed. The data in the less perceptible frequency band is modified, but not the high contrast pixels region and very high frequency component where human eyes are sensitive to changes and contrast. Finally, an inverse transformation is made to form the watermarked image. The sane principle may apply to watermarking a sound source, by which we should have good understanding of Human Auditory System (HAS). Similar to human eye, human ear exhibits a different sensibility to sounds depending on the frequency. It is almost insensitive to sound having frequency below 10Hz and above 20KHz.

To add details into original image that are not easily perceived, the fingerprint pattern recognition technology may be useful. Most fingerprint image comparison algorithms are using minutiae. Some important minutiae types available in the human finger are shown in Figure 4 and 5. The fingerprint image is first transformed into binary images through an ad-hoc filtering algorithm. The images obtained are then sampled into small sectors and submitted to a thinning process which allows for the ridge line thickness in order to achieve a one pixel width. Finally a simple image scan allows for locating the pixel that corresponds to minutiae (Figure 6). For the extracted information, position and direction of specific feature points like ridges ending, bifurcation are stored and kept as a signature for further comparison.


Figure 4 Minutiae Types for Human Figure