Web: http://arxiv.org/abs/2206.07321

June 16, 2022, 1:10 a.m. | Abderrahmen Amich, Ata Kaboudi, Birhanu Eshete

cs.LG updates on arXiv.org arxiv.org

Evasion attacks against machine learning models often succeed via iterative
probing of a fixed target model, whereby an attack that succeeds once will
succeed repeatedly. One promising approach to counter this threat is making a
model a moving target against adversarial inputs. To this end, we introduce
Morphence-2.0, a scalable moving target defense (MTD) powered by
out-of-distribution (OOD) detection to defend against adversarial examples. By
regularly moving the decision function of a model, Morphence-2.0 makes it
significantly challenging for repeated …

arxiv defense detection distribution evasion moving resilient

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