A multi-resolution fractal additive scheme for blind watermarking of 3D point data

Rahmes, Mark; Wilder, Kathy; Fox, Kevin M.

doi:10.1117/12.2009020

Cited by 3 publications

(1 citation statement)

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“…Thus, the affine transformations are not supported. Rames et al presented a fragile fractal scheme for watermarking of LiDAR data, which can handle a huge number of points [31]. Their method incorporates computationally intensive operations and is not robust to attacks.…”

Section: Previous and Related Workmentioning

confidence: 99%

3D Convex Hull-Based Registration Method for Point Cloud Watermark Extraction

Lipuš

Žalik

2019

Sensors

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Most 3D point cloud watermarking techniques apply Principal Component Analysis (PCA) to protect the watermark against affine transformation attacks. Unfortunately, they fail in the case of cropping and random point removal attacks. In this work, an alternative approach is proposed that solves these issues efficiently. A point cloud registration technique is developed, based on a 3D convex hull. The scale and the initial rigid affine transformation between the watermarked and the original point cloud can be estimated in this way to obtain a coarse point cloud registration. An iterative closest point algorithm is performed after that to align the attacked watermarked point cloud to the original one completely. The watermark can then be extracted from the watermarked point cloud easily. The extensive experiments confirmed that the proposed approach resists the affine transformation, cropping, random point removal, and various combinations of these attacks. The most dangerous is an attack with noise that can be handled only to some extent. However, this issue is common to the other state-of-the-art approaches.

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Section: Previous and Related Workmentioning

confidence: 99%