Robust watermarking of airborne LiDAR data

This paper presents a novel robust approach developed specially for watermarking airborne LiDAR data, which consist of a large cloud of geo-referenced points and has some unique characteristics. The approach consists of the following steps: (1) Defining the marker circular areas, in which the watermark bit will be embedded; (2) Dividing the marker circular areas uniformly into smaller circular areas by applying the sunflower seed distribution algorithm; (3) Using the points in these smaller circular areas to construct the input values for the Discrete Cosine Transformation (DCT); (4) Changing the last DCT coefficient; (5) Perform Inverse Discrete Cosine Transformation (IDCT), and perturbing the points within smaller circular areas according to the output values from this inverse transformation. Applying our approach, the watermark was dispersed into a set of points within the marker circular areas. The watermark bits are embedded multiple times in different marker circular areas. Thus, the robustness of the watermark was increased against various attacks. The watermark extraction process is practically the same, except in the final step, in which only the sign of each last DCT coefficient is checked, and decisions are made about the value of the watermark bits. Several experiments were performed to analyse the robustness of our watermarking schema against the most probable attacks.

[1]  Martin Isenburg,et al.  LASzip: Lossless Compression of Lidar Data , 2013 .

[2]  Frank Y. Shih,et al.  Digital Watermarking and Steganography: Fundamentals and Techniques , 2007 .

[3]  Caiming Zhang,et al.  A highly adaptable capacity and invisibility 3D watermarking based on four-points sets , 2010, MM&Sec '10.

[4]  B. Lipus,et al.  Lossy LAS file compression using uniform space division , 2012 .

[5]  Edward J. Delp,et al.  3D Object Processing: Compression, Indexing and Watermarking , 2008, J. Electronic Imaging.

[6]  Jana Dittmann,et al.  Proceedings of the 10th ACM workshop on Multimedia and security , 2008 .

[7]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[8]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[9]  Chung-Ming Wang,et al.  Data Hiding Approach for Point-Sampled Geometry , 2005, IEICE Trans. Commun..

[10]  William Puech,et al.  High capacity data hiding for 3D point clouds based on Static Arithmetic Coding , 2017, Multimedia Tools and Applications.

[11]  Domen Mongus,et al.  Ground and building extraction from LiDAR data based on differential morphological profiles and locally fitted surfaces , 2014 .

[12]  Ingemar J. Cox,et al.  Digital Watermarking and Steganography , 2014 .

[13]  Borut Žalik,et al.  Lossless progressive compression of LiDAR data using hierarchical grid level distribution , 2015 .

[14]  Domen Mongus,et al.  Point Density Evaluation of Airborne LiDAR Datasets , 2015, J. Univers. Comput. Sci..

[15]  Domen Mongus,et al.  Context-dependent detection of non-linearly distributed points for vegetation classification in airborne LiDAR , 2016 .

[16]  Jeng-Shyang Pan,et al.  A Reversible Data Hiding Scheme for 3D Point Cloud Model , 2006, 2006 IEEE International Symposium on Signal Processing and Information Technology.

[17]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[18]  Jean-Luc Dugelay,et al.  3D Object Processing , 2008 .

[19]  Gregory W. Wornell,et al.  Quantization index modulation: A class of provably good methods for digital watermarking and information embedding , 2001, IEEE Trans. Inf. Theory.

[20]  Domen Mongus,et al.  Efficient method for lossless LIDAR data compression , 2011 .

[21]  D. Lowe,et al.  Fast Matching of Binary Features , 2012, 2012 Ninth Conference on Computer and Robot Vision.

[22]  David G. Lowe,et al.  Scalable Nearest Neighbor Algorithms for High Dimensional Data , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Dafang Zhang,et al.  A robust watermarking scheme for 3D point cloud models using self-similarity partition , 2010, WCNIS.

[24]  Chung-Ming Wang,et al.  Reversible Data Hiding for Point-Sampled Geometry , 2007, J. Inf. Sci. Eng..

[25]  Przemyslaw Prusinkiewicz,et al.  The Algorithmic Beauty of Plants , 1990, The Virtual Laboratory.

[26]  J. Shan,et al.  Topographic laser ranging and scanning : principles and processing , 2008 .

[27]  B. Prabhakaran,et al.  Robust Blind Watermarking of Point-Sampled Geometry , 2009, IEEE Transactions on Information Forensics and Security.

[28]  Chung-Ming Wang,et al.  Steganography on point-sampled geometry , 2006, Comput. Graph..

[29]  H. Vogel.  A better way to construct the sunflower head , 1979 .

[30]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[31]  Caiming Zhang,et al.  A robust high-capacity affine-transformation-invariant scheme for watermarking 3D geometric models , 2012, TOMCCAP.

[32]  Qi Ke,et al.  An High-capacity Steganographic Scheme for 3D Point Cloud Models Using Self-similarity Partition , 2011 .

[33]  Atilla Baskurt,et al.  A Comprehensive Survey on Three-Dimensional Mesh Watermarking , 2008, IEEE Transactions on Multimedia.

[34]  Niko Lukač,et al.  Fast approximate k-nearest neighbours search using GPGPU , 2015 .

[35]  Tim Weyrich,et al.  Robust Watermarking of Point-Sampled Geometry , 2004 .

[36]  Mark Rahmes,et al.  A multi-resolution fractal additive scheme for blind watermarking of 3D point data , 2013, Defense, Security, and Sensing.