Abstract
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