论文标题
代码桥接分类器（CBC）：低或负面的间接辩护，用于使CNN分类器强大地针对对抗性攻击
Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
论文作者
论文摘要
在本文中，我们提出了代码桥接的分类器（CBC），这是一个使卷积神经网络（CNN）稳健地抵抗对抗攻击的框架，而无需增加甚至通过降低整体模型的计算复杂性。更具体地说，我们提出了一个堆叠的编码器 - 横向跨度模型，其中首先由Denoising自动编码器的编码器模块编码输入图像，然后由所得的潜在表示（不被解码）将其馈送到降低的复杂性CNN，以进行图像分类。我们说明，该网络不仅对对抗性示例更加健壮，而且与先前的ART防御相比，计算复杂性也明显降低。
In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses.
