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
Milani A.
;
2021-01-01
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.File in questo prodotto:
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