In the framework of Adversarial Machine Learning, several detection and protection techniques are used to characterize specific attack-defense scenarios. In this paper, we present universal, unrestricted black-box adversarial attacks based on a multi-objective nested evolutionary algorithm able to incorporate the detection rate and a measure of image quality into the attack building phase.

Combining Attack Success Rate and Detection Rate for effective Universal Adversarial Attacks

Baia A. E.;Milani A.;Poggioni V.
2021

Abstract

In the framework of Adversarial Machine Learning, several detection and protection techniques are used to characterize specific attack-defense scenarios. In this paper, we present universal, unrestricted black-box adversarial attacks based on a multi-objective nested evolutionary algorithm able to incorporate the detection rate and a measure of image quality into the attack building phase.
2021
978287587082-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1524631
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