  As asynchronous event data is more frequently engaged in various vision
tasks, the risk of backdoor attacks becomes more evident. However, research
into the potential risk associated with backdoor attacks in asynchronous event
data has been scarce, leaving related tasks vulnerable to potential threats.
This paper has uncovered the possibility of directly poisoning event data
streams by proposing Event Trojan framework, including two kinds of triggers,
i.e., immutable and mutable triggers. Specifically, our two types of event
triggers are based on a sequence of simulated event spikes, which can be easily
incorporated into any event stream to initiate backdoor attacks. Additionally,
for the mutable trigger, we design an adaptive learning mechanism to maximize
its aggressiveness. To improve the stealthiness, we introduce a novel loss
function that constrains the generated contents of mutable triggers, minimizing
the difference between triggers and original events while maintaining
effectiveness. Extensive experiments on public event datasets show the
effectiveness of the proposed backdoor triggers. We hope that this paper can
draw greater attention to the potential threats posed by backdoor attacks on
event-based tasks. Our code is available at
https://github.com/rfww/EventTrojan.
