Mesh Discriminative Features for 3D Steganalysis

Yang, Ying; Ivrissimtzis, Ioannis

Abstract

We propose a steganalytic algorithm for triangle meshes, based on the supervised training of a classifier by discriminative feature vectors. After a normalization step, the triangle mesh is calibrated by one step of Laplacian smoothing and then a feature vector is computed, encoding geometric information corresponding to vertices, edges and faces. For a given steganographic or watermarking algorithm, we create a training set containing unmarked meshes and meshes marked by that algorithm, and train a classifier using Quadratic Discriminant Analysis. The performance of the proposed method was evaluated on six well-known watermarking/steganographic schemes with satisfactory accuracy rates.

Citation

Yang, Y., & Ivrissimtzis, I. (2014). Mesh Discriminative Features for 3D Steganalysis. ACM Transactions on Multimedia Computing, Communications and Applications, 10(3), Article 27. https://doi.org/10.1145/2535555

• Journal Article Type: Article
• Acceptance Date: Sep 25, 2013
• Online Publication Date: Apr 17, 2014
• Publication Date: Apr 17, 2014
• Deposit Date: Feb 24, 2016
• Publicly Available Date: Mar 1, 2016
• Journal: ACM Transactions on Multimedia Computing, Communications and Applications
• Print ISSN: 1551-6857
• Electronic ISSN: 1551-6865
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 3
• Article Number: 27
• DOI: https://doi.org/10.1145/2535555
• Public URL: https://durham-repository.worktribe.com/output/1390781