BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron

TL;DR

This paper introduces BadSNN, a novel backdoor attack on spiking neural networks that exploits neuron hyperparameters to inject malicious behaviors, demonstrating superior performance and robustness against defenses.

Contribution

The paper presents BadSNN, the first backdoor attack specifically targeting SNNs by leveraging hyperparameter variations and trigger optimization techniques.

Findings

BadSNN achieves high attack success rates on various datasets and architectures.

It outperforms existing data poisoning backdoor attacks.

The attack remains effective against common backdoor mitigation methods.

Abstract

Spiking Neural Networks (SNNs) are energy-efficient counterparts of Deep Neural Networks (DNNs) with high biological plausibility, as information is transmitted through temporal spiking patterns. The core element of an SNN is the spiking neuron, which converts input data into spikes following the Leaky Integrate-and-Fire (LIF) neuron model. This model includes several important hyperparameters, such as the membrane potential threshold and membrane time constant. Both the DNNs and SNNs have proven to be exploitable by backdoor attacks, where an adversary can poison the training dataset with malicious triggers and force the model to behave in an attacker-defined manner. Yet, how an adversary can exploit the unique characteristics of SNNs for backdoor attacks remains underexplored. In this paper, we propose \textit{BadSNN}, a novel backdoor attack on spiking neural networks that exploits hyperparameter variations of spiking neurons to inject backdoor behavior into the model. We further propose a trigger optimization process to achieve better attack performance while making trigger patterns less perceptible. \textit{BadSNN} demonstrates superior attack performance on various datasets and architectures, as well as compared with state-of-the-art data poisoning-based backdoor attacks and robustness against common backdoor mitigation techniques. Codes can be found at https://github.com/SiSL-URI/BadSNN.