Convolutional neural networks (CNN) define the state-of-the-art solution on many perceptual tasks. However, current CNN approaches largely remain vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to the human eye. In recent years, various approaches have been proposed to defend CNNs against such attacks, for example by model hardening or by adding explicit defence mechanisms. Thereby, a small "detector" is included in the network and trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. In this work, we propose a simple and light-weight detector, which leverages recent findings on the relation between networks' local intrinsic dimensionality (LID) and adversarial attacks. Based on a re-interpretation of the LID measure and several simple adaptations, we surpass the state-of-the-art on adversarial detection by a significant margin and reach almost perfect results in terms of F1-score for several networks and datasets. Sources available at: https://github.com/adverML/multiLID

翻译：卷积神经网络（CNN）定义了许多感知任务上的最优解决方案。然而，当前的CNN方法在面对专门为欺骗系统而精心设计、同时对人眼几乎不可察觉的输入对抗性扰动时，仍然容易受到攻击。近年来，研究人员提出了多种方法来防御CNN免受此类攻击，例如通过模型加固或添加显式防御机制。具体而言，在网络中加入一个轻量级“检测器”，并针对区分真实数据与包含对抗性扰动的数据这一二分类任务进行训练。在本工作中，我们提出了一种简单且轻量的检测器，它利用了关于网络局部固有维度（LID）与对抗攻击之间关系的最新发现。基于对LID度量的重新解释以及若干简单调整，我们在对抗性检测方面显著超越了当前最优水平，并在多个网络和数据集上达到了近乎完美的F1分数。源代码见：https://github.com/adverML/multiLID