Real-Time Adversarial Attacks

By Yuan Gong, Boyang Li, Christian Poellabauer, Yiyu Shi. 2019

68 citations recorded by the source corpus.

In recent years, many efforts have demonstrated that modern machine learning algorithms are vulnerable to adversarial attacks, where small, but carefully crafted, perturbations on the input can make them fail. While these attack methods are very effective, they only focus on scenarios where the target model takes static input, i.e., an attacker can observe the entire original sample and then add a perturbation at any point of the sample. These attack approaches are not applicable to situations where the target model takes streaming input, i.e., an attacker is only able to observe past data points and add perturbations to the remaining (unobserved) data points of the input. In this paper, we propose a real-time adversarial attack scheme for machine learning models with streaming inputs.

Subfields: Artificial Intelligence, Spectroscopy, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Safety, Robustness & Interpretability

Citations in the corpus

Online Adversarial Attacks (2021, background): “To the best of our knowledge, the only existing approaches that craft adversarial examples on streaming data [Gong et al., 2019a, Lin et al., 2017, Sun et al., 2020] require multiple passes through a data stream and thus cannot be applied in a realistic online setting where an adversary is forced…”
ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems (2019, background): “Despite their convenience, VCSs raise new security concerns due to their vulnerability to multiple types of spoofing attacks, such as replay attacks, self-triggered attacks [1], hidden voice commands [2, 3], and audio adversarial examples [4, 5, 6, 7].”