Optimal Transport for Secure Spread-Spectrum ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Optimal Transport for Secure Spread-Spectrum Watermarking of Still Images
Auteur(s) :
Mathon, Benjamin [Auteur correspondant]
Computer Vision and Multimedia Laboratory [Geneve] [CVML]
GIPSA - Communication Information and Complex Systems [GIPSA-CICS]
Cayre, François [Auteur]
GIPSA - Communication Information and Complex Systems [GIPSA-CICS]
Bas, Patrick [Auteur]
LAGIS-SI
Macq, Benoît [Auteur]
Computer Vision and Multimedia Laboratory [Geneve] [CVML]
GIPSA - Communication Information and Complex Systems [GIPSA-CICS]
Cayre, François [Auteur]
GIPSA - Communication Information and Complex Systems [GIPSA-CICS]
Bas, Patrick [Auteur]
LAGIS-SI
Macq, Benoît [Auteur]
Titre de la revue :
IEEE Transactions on Image Processing
Pagination :
1694-1705
Éditeur :
Institute of Electrical and Electronics Engineers
Date de publication :
2014-02-11
ISSN :
1057-7149
Mot(s)-clé(s) en anglais :
Watermarking
Digital images
Transportation theory
Digital images
Transportation theory
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
This article studies the impact of secure watermark embedding in digital images by proposing a practical implementation of secure spread-spectrum watermarking using distortion optimization. Because strong security properties ...
Lire la suite >This article studies the impact of secure watermark embedding in digital images by proposing a practical implementation of secure spread-spectrum watermarking using distortion optimization. Because strong security properties (key-security and subspace-security) can be achieved using Natural Watermarking (NW) since this particular embedding lets the distribution of the host and watermarked signals unchanged, we use elements of transportation theory to minimize the global distortion (MSE). Next, we apply this new modulation, called Transportation Natural Watermarking (TNW), to design a secure watermarking scheme for grayscale images. TNW uses a multiresolution image decomposition combined with a multiplicative embedding which is taken into account at the distribution level. We show that the distortion solely relies on the variance of the wavelet subbands used during the embedding. In order to maximize a target robustness after JPEG compression, we select different combinations of subbands offering the lowest BERs for a target PSNR ranging from 35 to 55 dB and we propose an algorithm to select them. The use of transportation theory also provides an average PSNR gain of 3.6 dB on PSNR with respect to the previous embedding for a set of 2,000 images.Lire moins >
Lire la suite >This article studies the impact of secure watermark embedding in digital images by proposing a practical implementation of secure spread-spectrum watermarking using distortion optimization. Because strong security properties (key-security and subspace-security) can be achieved using Natural Watermarking (NW) since this particular embedding lets the distribution of the host and watermarked signals unchanged, we use elements of transportation theory to minimize the global distortion (MSE). Next, we apply this new modulation, called Transportation Natural Watermarking (TNW), to design a secure watermarking scheme for grayscale images. TNW uses a multiresolution image decomposition combined with a multiplicative embedding which is taken into account at the distribution level. We show that the distortion solely relies on the variance of the wavelet subbands used during the embedding. In order to maximize a target robustness after JPEG compression, we select different combinations of subbands offering the lowest BERs for a target PSNR ranging from 35 to 55 dB and we propose an algorithm to select them. The use of transportation theory also provides an average PSNR gain of 3.6 dB on PSNR with respect to the previous embedding for a set of 2,000 images.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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