Event Trojan: Asynchronous Event-based Backdoor Attacks
Ruofei Wang, Qing Guo, Haoliang Li, and Renjie Wan

TL;DR
This paper introduces Event Trojan, a novel backdoor attack framework for asynchronous event data in vision tasks, demonstrating effective triggers that threaten current event-based systems and highlighting the need for improved defenses.
Contribution
The paper proposes a new backdoor attack method for event data, including immutable and mutable triggers with an adaptive learning mechanism and a stealthy loss function, advancing security research in event-based vision.
Findings
Effective backdoor triggers demonstrated on public datasets
Mutable triggers with adaptive learning maximize attack success
Stealthy triggers minimize detection while maintaining effectiveness
Abstract
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…
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Taxonomy
TopicsPhysical Unclonable Functions (PUFs) and Hardware Security · Cryptographic Implementations and Security · Advanced Malware Detection Techniques
MethodsSoftmax · Attention Is All You Need
