Anti-cropping blind resynchronization for 3D watermarking

Rolland-Neviere, Xavier; Doerr, Gwenaël; Alliez, Pierre

doi:10.1109/icassp.2015.7178261

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…For any watermarking system, the basic requirements are low error distortion and high robustness. The existing nodedomain graph watermarking methods are also focused on the watermarking robustness against the attacks [5][6][7][8][9][10] as well as minimising the distortion [11][12][13]. Similarly in our previous work, we have proposed models minimising the embedding distortion [14] and making robust for scalable decoding attacks [15,16] for general spread spectrum watermarking.…”

Section: Introductionmentioning

confidence: 97%

Graph Spectral Domain Blind Watermarking

Al-Khafaji

Abhayaratne

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes the first ever graph spectral domain blind watermarking algorithm. We explore the recently developed graph signal processing for spread-spectrum watermarking to authenticate the data recorded on non-Cartesian grids, such as sensor data, 3D point clouds, Lidar scans and mesh data. The choice of coefficients for embedding the watermark is driven by the model for minimisation embedding distortion and the robustness model. The distortion minimisation model is proposed to reduce the watermarking distortion by establishing the relationship between the error distortion using mean square error and the selected Graph Fourier coefficients to embed the watermark. The robustness model is proposed to improve the watermarking robustness against the attacks by establishing the relationship between the watermark extraction and the effect of the attacks, namely, additive noise and nodes data deletion. The proposed models were verified by the experimental results.

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