Accès chercheur

EEDIS Laboratory

Evolutionary Engineering


Distributed Information Systems

Réseaux et Communication

Sécurité et Multimédia

Ingénierie des Connaissances

Data Mining & Web Intelligent

Interopérabilité des Systèmes d’information
& Bases de données

Développement Orienté Service

Visual Quality and Security Assessment of Perceptually Encrypted Images Based on Multi-Output Deep Neural Network

Auteurs: » Mamadou Keita
» Hamidouche Wassim
Type : Conférence Internationale
Nom de la conférence : Conférence 2021 9th European Workshop on Visual Information Processing (EUVIP)
Lieu : Pays:
Lien : »
Publié le : 23-06-2021

Encryptionhas became an indispensable technique for image/video-based applications. Thishas led to the development of many image encryption algorithms, such asperceptual/selective encryption methods which represent an effective way forthe security and confidentiality of images. However, few studies focus onvisual security metric, which is very important tool for evaluating theeffectiveness of these encryption methods. Most of the adopted metrics are theclassical randomness-based measures or the objective image quality assessmentmetrics. However, these metrics showed their limits as a visual securitymetric, because they do not deal with the content intelligibility, which is oneof the key security requirements. Consequently, in this paper, we propose ano-reference (NR) visual security metric for perceptually encrypted imagesbased on multi-output learning called VSMML. The proposed metricconsists â€¦

Tous droits réservés - © 2019 EEDIS Laboratory