Adversarial Example Generation using Evolutionary Multi-objective Optimization

Authors: Takahiro Suzuki, Shingo Takeshita, Satoshi Ono
Published: 2019-12-30

Source: https://arxiv.org/abs/2001.05844

PDF: https://arxiv.org/pdf/2001.05844

Labels Predicted by AI

敵対的学習 多目的最適化 効果的な摂動手法

Please note that these labels were automatically added by AI. Therefore, they may not be entirely accurate.
For more details, please see the About the Literature Database page.

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 to produce AEs. Thanks to EMO’s property of population based-search, the proposed method produces various types of AEs involving ones locating between AEs generated by the previous two approaches, which helps to know the characteristics of a target model or to know unknown attack patterns. Experimental results showed the potential of the proposed method, e.g., it can generate robust AEs and, with the aid of DCT-based perturbation pattern generation, AEs for high resolution images.