论文标题
时间序列的通用傅立叶攻击
Universal Fourier Attack for Time Series
论文作者
论文摘要
已经提出并使用图像和音频数据对各种各样的对抗攻击进行了探索。众所周知，当攻击者可以直接操纵模型的输入时，这些攻击很容易生成，但是在现实世界中实现了更加困难。在本文中，我们提出了通用的通用时间不变攻击通用时间序列数据，以便该攻击具有主要由原始数据中存在的频率组成的频谱。攻击的通用性使其快速易于实现，因为不需要将其添加到输入中，而时间不变性对于现实世界部署很有用。此外，频率约束确保攻击可以承受过滤。我们证明了攻击在两个不同领域的有效性：语音识别和意外的辐射排放，并表明攻击对共同的转换和能力的防御管道是强大的。
A wide variety of adversarial attacks have been proposed and explored using image and audio data. These attacks are notoriously easy to generate digitally when the attacker can directly manipulate the input to a model, but are much more difficult to implement in the real-world. In this paper we present a universal, time invariant attack for general time series data such that the attack has a frequency spectrum primarily composed of the frequencies present in the original data. The universality of the attack makes it fast and easy to implement as no computation is required to add it to an input, while time invariance is useful for real-world deployment. Additionally, the frequency constraint ensures the attack can withstand filtering. We demonstrate the effectiveness of the attack in two different domains, speech recognition and unintended radiated emission, and show that the attack is robust against common transform-and-compare defense pipelines.
