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Morphence: Moving Target Defense Against Adversarial Examples. (arXiv:2108.13952v4 [cs.LG] UPDATED)
Feb. 21, 2022, 2:20 a.m. | Abderrahmen Amich, Birhanu Eshete
cs.CR updates on arXiv.org arxiv.org
Robustness to adversarial examples of machine learning models remains an open
topic of research. Attacks often succeed by repeatedly probing a fixed target
model with adversarial examples purposely crafted to fool it. In this paper, we
introduce Morphence, an approach that shifts the defense landscape by making a
model a moving target against adversarial examples. By regularly moving the
decision function of a model, Morphence makes it significantly challenging for
repeated or correlated attacks to succeed. Morphence deploys a pool …
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