论文标题
使用进化多目标优化的对抗性示例生成
Adversarial Example Generation using Evolutionary Multi-objective Optimization
论文作者
论文摘要
本文提出了基于进化的多目标优化（EMO）的对抗示例（AE）设计方法，该方法在黑框设置下执行。以前的基于梯度的方法通过更改目标图像的所有像素来产生AE，而先前基于EC的方法会更改少量像素以产生AE。得益于emo的基于人群搜索的属性，提出的方法产生了各种AE，涉及的AE涉及前两种方法生成的AE之间的AE，这有助于了解目标模型的特征或了解未知的攻击模式。实验结果表明，该方法的潜力，例如，它可以生成健壮的AE，并借助基于DCT的扰动模式产生，用于高分辨率图像的AE。
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.
