What if Adversarial Samples were Digital Images?
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
What if Adversarial Samples were Digital Images?
Auteur(s) :
Bonnet, Benoît [Auteur]
Creating and exploiting explicit links between multimedia fragments [LinkMedia]
Furon, Teddy [Auteur]
Creating and exploiting explicit links between multimedia fragments [LinkMedia]
Bas, Patrick [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Creating and exploiting explicit links between multimedia fragments [LinkMedia]
Furon, Teddy [Auteur]
Creating and exploiting explicit links between multimedia fragments [LinkMedia]
Bas, Patrick [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
IH&MMSEC 2020 - 8th ACM Workshop on Information Hiding and Multimedia Security
Ville :
Denver
Pays :
France
Date de début de la manifestation scientifique :
2020-06-22
Titre de la revue :
IH&MMSec '20: Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security
Éditeur :
ACM
Mot(s)-clé(s) en anglais :
Image classification
adversarial samples
neural networks
adversarial samples
neural networks
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Cryptographie et sécurité [cs.CR]
Informatique [cs]/Cryptographie et sécurité [cs.CR]
Résumé en anglais : [en]
Although adversarial sampling is a trendy topic in computer vision , very few works consider the integral constraint: The result of the attack is a digital image whose pixel values are integers. This is not an issue at ...
Lire la suite >Although adversarial sampling is a trendy topic in computer vision , very few works consider the integral constraint: The result of the attack is a digital image whose pixel values are integers. This is not an issue at first sight since applying a rounding after forging an adversarial sample trivially does the job. Yet, this paper shows theoretically and experimentally that this operation has a big impact. The adversarial perturbations are fragile signals whose quantization destroys its ability to delude an image classifier. This paper presents a new quantization mechanism which preserves the adversariality of the perturbation. Its application outcomes to a new look at the lessons learnt in adversarial sampling.Lire moins >
Lire la suite >Although adversarial sampling is a trendy topic in computer vision , very few works consider the integral constraint: The result of the attack is a digital image whose pixel values are integers. This is not an issue at first sight since applying a rounding after forging an adversarial sample trivially does the job. Yet, this paper shows theoretically and experimentally that this operation has a big impact. The adversarial perturbations are fragile signals whose quantization destroys its ability to delude an image classifier. This paper presents a new quantization mechanism which preserves the adversariality of the perturbation. Its application outcomes to a new look at the lessons learnt in adversarial sampling.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
Fichiers
- https://hal.archives-ouvertes.fr/hal-02553006v2/document
- Accès libre
- Accéder au document
- https://hal.archives-ouvertes.fr/hal-02553006v2/document
- Accès libre
- Accéder au document
- https://hal.archives-ouvertes.fr/hal-02553006v2/document
- Accès libre
- Accéder au document
- document
- Accès libre
- Accéder au document
- main_finalWOcopy.pdf
- Accès libre
- Accéder au document
- main_finalWOcopy.pdf
- Accès libre
- Accéder au document