Adversarial Example Generation using Evolutionary Multi-objective Optimization

30 Dec 2019Takahiro SuzukiShingo TakeshitaSatoshi Ono

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 to produce AEs... (read more)

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