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）定义了诸多感知任务中的最优解决方案。然而，当前的CNN方法在面对特意设计以欺骗系统、同时对人眼几乎不可见的输入对抗扰动时，仍然存在显著脆弱性。近年来，研究者提出了多种防御此类攻击的方法，例如通过模型加固或添加显式防御机制。其中，在网络中嵌入一个轻量级"检测器"，并训练其执行区分真实数据与包含对抗扰动数据的二分类任务。本文提出一种简单而轻量的检测器，该检测器利用网络局部固有维度（LID）与对抗攻击之间关系的最新发现。基于对LID度量的重新解释及若干简单改进，我们在对抗检测性能上显著超越了现有最优方法，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