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T-Backdoor: Exploiting Temporal Redundancy in Neuromorphic Data for Spike-preserving Backdoor Attacks on SNNs
Abdullah Arafat Miah, Kevin Vu, Yu Bi
Backdoor attacks are a serious security threat to deep neural networks (DNNs) and remain largely underexplored for spiking neural networks (SNNs). Existing attacks primarily introd…
Lite-BD: A Lightweight Black-box Backdoor Defense via Reviving Multi-Stage Image Transformations
Abdullah Arafat Miah, Yu Bi
Deep Neural Networks (DNNs) are vulnerable to backdoor attacks. Due to the nature of Machine Learning as a Service (MLaaS) applications, black-box defenses are more practical than…
BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron
Abdullah Arafat Miah, Kevin Vu, Yu Bi
Spiking Neural Networks (SNNs) are energy-efficient counterparts of Deep Neural Networks (DNNs) with high biological plausibility, as information is transmitted through temporal sp…
Exploiting the Vulnerability of Large Language Models via Defense-Aware Architectural Backdoor
Abdullah Arafat Miah, Yu Bi
Deep neural networks (DNNs) have long been recognized as vulnerable to backdoor attacks. By providing poisoned training data in the fine-tuning process, the attacker can implant a…