Adversarial Example Generation using Evolutionary Multi-objective Optimization
Published in arXiv (Cornell University) • Dec 30, 2019
NobleIDNI2P81W17R82S44
Authors:,,
Takahiro Suzuki
Shingo Takeshita
Satoshi Ono
Abstract
This paper proposes Evolutionary Multi-objective Optimization (EMO)-based Adversarial Example (AE) design method that performs under black-box setting. Previous gradient-based methods produce AEs by changing all pixels of a target image, while previous EC-based method changes small number of pixels ...
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