Generating Adversarial Images in Quantized Domains

Bonnet, Benoit; Furon, Teddy; Bas, Patrick

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1109/TIFS.2021.3138616

Title :

Generating Adversarial Images in Quantized Domains

Author(s) :

Bonnet, Benoit [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]

Journal title :

IEEE Transactions on Information Forensics and Security

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2022

ISSN :

1556-6013

HAL domain(s) :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

English abstract : [en]

Many adversarial attacks produce floating-point tensors which are no longer adversarial when converted to raster or JPEG images due to rounding. This paper proposes a method dedicated to quantize adversarial perturbations. ...
Show more >Many adversarial attacks produce floating-point tensors which are no longer adversarial when converted to raster or JPEG images due to rounding. This paper proposes a method dedicated to quantize adversarial perturbations. This "smart" quantization is conveniently implemented as versatile post-processing. It can be used on top of any white-box attack targeting any model. Its principle is tantamount to a constrained optimization problem aiming to minimize the quantization error while keeping the image adversarial after quantization. A Lagrangian formulation is proposed and an appropriate search of the Lagrangian multiplier enables to increase the success rate. We also add a control mechanism of the ∞-distortion. Our method operates in both spatial and JPEG domains with little complexity. This study shows that forging adversarial images is not a hard constraint: our quantization does not introduce any extra distortion. Moreover, adversarial images quantized as JPEG also challenge defenses relying on the robustness of neural networks against JPEG compression.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

Utilisation de grandes bases d'images hétérogènes en stéganalyse pour se rapprocher d'un contexte opérationnel
Outils pour la détection de manipulation d'images numériques.
Sécurité de l'Intelligence Artificielle pour des Applications Défense

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