By the same authors

3D Mesh Steganalysis using local shape features

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

3D Mesh Steganalysis using local shape features. / Li, Zhenyu; Bors, Adrian Gheorghe.

Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. p. 2144-2148.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Li, Z & Bors, AG 2016, 3D Mesh Steganalysis using local shape features. in Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 2144-2148.

APA

Li, Z., & Bors, A. G. (2016). 3D Mesh Steganalysis using local shape features. In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2144-2148). IEEE.

Vancouver

Li Z, Bors AG. 3D Mesh Steganalysis using local shape features. In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2016. p. 2144-2148

Author

Li, Zhenyu ; Bors, Adrian Gheorghe. / 3D Mesh Steganalysis using local shape features. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. pp. 2144-2148

Bibtex - Download

@inproceedings{9310ad9ddc364b438fd3f3f9dc84980d,
title = "3D Mesh Steganalysis using local shape features",
abstract = "Steganalysis aims to identify those changes performed in aspecific media with the intention to hide information. In thispaper we assess the efficiency, in finding hidden information,of several local feature detectors. In the proposed 3D ste-ganalysis approach we first smooth the cover object and itscorresponding stego-object obtained after embedding a givenmessage. We use various operators in order to extract lo-cal features from both the cover and stego-objects, and theirsmoothed versions. Machine learning algorithms are thenused for learning to discriminate between those 3D objectswhich are used as carriers of hidden information and thoseare not used. The proposed 3D steganalysis methodology isshown to provide superior performance to other approachesin a well known database of 3D objects.",
author = "Zhenyu Li and Bors, {Adrian Gheorghe}",
note = "{\circledC} IEEE, 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details",
year = "2016",
month = "5",
language = "English",
pages = "2144--2148",
booktitle = "Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP)",
publisher = "IEEE",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - 3D Mesh Steganalysis using local shape features

AU - Li, Zhenyu

AU - Bors, Adrian Gheorghe

N1 - © IEEE, 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details

PY - 2016/5

Y1 - 2016/5

N2 - Steganalysis aims to identify those changes performed in aspecific media with the intention to hide information. In thispaper we assess the efficiency, in finding hidden information,of several local feature detectors. In the proposed 3D ste-ganalysis approach we first smooth the cover object and itscorresponding stego-object obtained after embedding a givenmessage. We use various operators in order to extract lo-cal features from both the cover and stego-objects, and theirsmoothed versions. Machine learning algorithms are thenused for learning to discriminate between those 3D objectswhich are used as carriers of hidden information and thoseare not used. The proposed 3D steganalysis methodology isshown to provide superior performance to other approachesin a well known database of 3D objects.

AB - Steganalysis aims to identify those changes performed in aspecific media with the intention to hide information. In thispaper we assess the efficiency, in finding hidden information,of several local feature detectors. In the proposed 3D ste-ganalysis approach we first smooth the cover object and itscorresponding stego-object obtained after embedding a givenmessage. We use various operators in order to extract lo-cal features from both the cover and stego-objects, and theirsmoothed versions. Machine learning algorithms are thenused for learning to discriminate between those 3D objectswhich are used as carriers of hidden information and thoseare not used. The proposed 3D steganalysis methodology isshown to provide superior performance to other approachesin a well known database of 3D objects.

M3 - Conference contribution

SP - 2144

EP - 2148

BT - Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

ER -