We propose two training techniques for improving the robustness of Neural Networks to adversarial attacks, i.e. manipulations of the inputs that are maliciously crafted to fool networks into incorrect predictions. Both methods are independent of the chosen attack and leverage random projections of the original inputs, with the purpose of exploiting both dimensionality reduction and some characteristic geometrical properties of adversarial perturbations. The first technique is called RP-Ensemble and consists of an ensemble of networks trained on multiple projected versions of the original inputs. The second one, named RP-Regularizer, adds instead a regularization term to the training objective.

Random Projections for Improved Adversarial Robustness

Ginevra Carbone
;
Luca Bortolussi
2021

Abstract

We propose two training techniques for improving the robustness of Neural Networks to adversarial attacks, i.e. manipulations of the inputs that are maliciously crafted to fool networks into incorrect predictions. Both methods are independent of the chosen attack and leverage random projections of the original inputs, with the purpose of exploiting both dimensionality reduction and some characteristic geometrical properties of adversarial perturbations. The first technique is called RP-Ensemble and consists of an ensemble of networks trained on multiple projected versions of the original inputs. The second one, named RP-Regularizer, adds instead a regularization term to the training objective.
https://ieeexplore.ieee.org/document/9534346
File in questo prodotto:
File Dimensione Formato  
2102.09230.pdf

accesso aperto

Tipologia: Altro materiale allegato
Licenza: Digital Rights Management non definito
Dimensione 597.28 kB
Formato Adobe PDF
597.28 kB Adobe PDF Visualizza/Apri
Random_Projections_for_Improved_Adversarial_Robustness.pdf

non disponibili

Tipologia: Documento in Versione Editoriale
Licenza: Copyright Editore
Dimensione 2.27 MB
Formato Adobe PDF
2.27 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11368/2990606
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact