1. Robust Invisible Watermarking of Volume Data Y. Wu 1, X. Guan 2, M. S. Kankanhalli 1, Z. Huang 1 NUS Logo 12

2. 5 September 2000NUROP Congress 20002 Introduction  Copyright protection of digital visual content The concern over the copyright violation is more serious because: the creation of content is not cheap the growth of computer networks and the popularity of WWW enables faster and error-free data transfer digital visual content enables faster and more efficient storage, transfer and processing the unauthorized duplication and manipulation of digital contents are easy but difficult to detect

3. 5 September 2000NUROP Congress 20003 Watermarking Solution  Digital watermarking To cement into the digital visual contents a secondary signal containing ownership information that can be recovered or the manipulation of the contents can be detected  Types of digital watermarking –Authentication/localization of changes Fragile watermarks –Ownership protection Robust watermarks –Tracing of distribution channels Fingerprints

4. 5 September 2000NUROP Congress 20004 Previous Works  Frequency domain transformation (Cox et al., 1999, Proceeding Of IEEE, 87(7))  Digital images (Kankanhalli et al., ACM MM ’98)  Digital videos (Dittman et al., ACM MM ’98)  Polygon meshes (Praun et al., SIGGRAPH ’99) Spatial Domain Frequency Domain DCT

5. 5 September 2000NUROP Congress 20005 Watermark Embedding In 5 Steps: Derive the 3D DCT coefficients of the 3D volume Spread the watermark information bits with a pseudo-random noise signal sequence to get the watermark sequence Derive the 3D DCT coefficients of the watermark sequence Construct the watermarked volume in the frequency domain Compute the watermarked 3D volume by 3D Inverse DCT

6. 5 September 2000NUROP Congress 20006 Watermark Detection In 4 Steps: Compute the residual volume data’s DCT coefficients Derive the residual watermark sequence by 3D Inverse DCT Correlate the residual watermark sequence with the original pseudo-random signal sequence Derive the watermark information bits by statistics on the correlation coefficients

7. 5 September 2000NUROP Congress 20007 Algorithm Analysis  Properties of watermark embedding The rendering of watermarked volume is indistinguishable from the original’s rendering The watermark is noise-like, which makes it difficult to detect without compromising on the rendering results  Properties of watermark detection Original volume data is required The unshifted pseudo-random noise signal sequence used in embedding is required

8. 5 September 2000NUROP Congress 20008 Robustness Test Test results show that the algorithm is good against the following attacks: Geometric distortions Addition of constant offset of voxel values Addition of Gaussian or non-Gaussian noise Linear filtering and non-linear filtering Local exchange of voxel rows/columes Quantization and requantization D/A and A/D conversions

9. 5 September 2000NUROP Congress 20009 BeforeAfter Volume rendering by PKVox Original skull volume: 68  64  64 grey-scaled Marked skull volume: L wm > 150 bytes