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A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking

Yang, Ying; Pintus, Ruggero; Rushmeier, Holly; Ivrissimtzis, Ioannis

A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking Thumbnail


Ying Yang

Ruggero Pintus

Holly Rushmeier


We propose a simple yet efficient steganalytic algorithm for watermarks embedded by two state-of-the-art 3D watermarking algorithms by Cho et al. The main observation is that while in a clean model the means/variances of Cho et al.’s normalized histogram bins are expected to follow a Gaussian distribution, in a marked model their distribution will be bimodal. The proposed algorithm estimates the number of bins through an exhaustive search and then the presence of a watermark is decided by a tailor made normality test or a t-test. We also propose a modification of Cho et al.’s watermarking algorithms with the watermark embedded by changing the histogram of the radial coordinates of the vertices. Rather than targeting a continuous statistics such as the mean or variance of the values in a bin, the proposed watermarking modifies a discrete statistic, which here is the height of the histogram bin, to achieve watermark embedding. Experimental results demonstrate that the modified algorithm offers not only better resistance against the steganalytic attack we developed, but also an improved robustness/capacity trade-off.


Yang, Y., Pintus, R., Rushmeier, H., & Ivrissimtzis, I. (2017). A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking. IEEE Transactions on Visualization and Computer Graphics, 23(2), 1002-2626.

Journal Article Type Article
Acceptance Date Jan 24, 2016
Online Publication Date Feb 4, 2016
Publication Date Feb 1, 2017
Deposit Date Feb 24, 2016
Publicly Available Date Feb 29, 2016
Journal IEEE Transactions on Visualization and Computer Graphics
Print ISSN 1077-2626
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 2
Pages 1002-2626
Related Public URLs


Accepted Journal Article (4 Mb)

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