University Sétif 1 FERHAT ABBAS Faculty of Sciences
Détail de l'auteur
Auteur El Houes Benali |
Documents disponibles écrits par cet auteur
Ajouter le résultat dans votre panier Affiner la recherche
Titre : Identification of falsification of multimedia data Type de document : texte imprimé Auteurs : Feriel Khedidja Haddad, Auteur ; El Houes Benali ; Bilal Benmessahel, Directeur de thèse Editeur : Sétif:UFS Année de publication : 2023 Importance : 1 vol (59 f.) Format : 29 cm Langues : Anglais (eng) Catégories : Thèses & Mémoires:Informatique Mots-clés : Image forgery detection
Block Based Approach
PatchMatch algorithmIndex. décimale : 004 - Informatique Résumé : Image forgery detection is a complex and challenging problem, with many different approaches
to solving it. However, all of these approaches share the same goal: to detect and localize any
forgeries that may be present in an image.
Copy-move forgery detection (CMFD) is one of the most widely used approaches to image
forgery detection,it is a common type of image manipulation where a portion of an image is copied
and pasted into another location in the same image. This type of forgery can be used to hide
or add objects to an image, or to change the overall content of the image. In this thesis, We
first inves-tigate the problems and the challenges of the existing algorithms to detect copy-move
forgery in digital images , and as we know that Digital image forgery is a growing problem because
the technology to forge images is becoming more accessible. Forgers can now easily copy and
paste sections of images, or even create entirely fake images. This makes it difficult to determine
whether an image is authentic or forged.We propose strategies and applications such Block Based
Approach or Sensor Pattern noise or the PatchMatch algorithm .We further focus on the convolutional neural network (CNN) , we talked about CNN architecture and its model. In the other
part we have used the COMOFOD and CASIA datasets for our research.That means that we have
used different datasets to train and test our model for copy-move forgery detection.We believe that
using different datasets is important for ensuring the accuracy and robustness of our model.Côte titre : MAI/0813
En ligne : https://drive.google.com/file/d/1Ao-vU8gSbOjfaerTW12jOTaL7a7O-YjF/view?usp=drive [...] Format de la ressource électronique : Identification of falsification of multimedia data [texte imprimé] / Feriel Khedidja Haddad, Auteur ; El Houes Benali ; Bilal Benmessahel, Directeur de thèse . - [S.l.] : Sétif:UFS, 2023 . - 1 vol (59 f.) ; 29 cm.
Langues : Anglais (eng)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Image forgery detection
Block Based Approach
PatchMatch algorithmIndex. décimale : 004 - Informatique Résumé : Image forgery detection is a complex and challenging problem, with many different approaches
to solving it. However, all of these approaches share the same goal: to detect and localize any
forgeries that may be present in an image.
Copy-move forgery detection (CMFD) is one of the most widely used approaches to image
forgery detection,it is a common type of image manipulation where a portion of an image is copied
and pasted into another location in the same image. This type of forgery can be used to hide
or add objects to an image, or to change the overall content of the image. In this thesis, We
first inves-tigate the problems and the challenges of the existing algorithms to detect copy-move
forgery in digital images , and as we know that Digital image forgery is a growing problem because
the technology to forge images is becoming more accessible. Forgers can now easily copy and
paste sections of images, or even create entirely fake images. This makes it difficult to determine
whether an image is authentic or forged.We propose strategies and applications such Block Based
Approach or Sensor Pattern noise or the PatchMatch algorithm .We further focus on the convolutional neural network (CNN) , we talked about CNN architecture and its model. In the other
part we have used the COMOFOD and CASIA datasets for our research.That means that we have
used different datasets to train and test our model for copy-move forgery detection.We believe that
using different datasets is important for ensuring the accuracy and robustness of our model.Côte titre : MAI/0813
En ligne : https://drive.google.com/file/d/1Ao-vU8gSbOjfaerTW12jOTaL7a7O-YjF/view?usp=drive [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0813 MAI/0813 Mémoire Bibliothéque des sciences Anglais Disponible
Disponible