1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack
Md Nabi Newaz Khan, Abdullah Arafat Miah, Yu Bi
Graph neural network (GNN) have demonstrated exceptional performance in solving critical problems across diverse domains yet remain susceptible to backdoor attacks. Existing studie…
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…
NoiseAttack: An Evasive Sample-Specific Multi-Targeted Backdoor Attack Through White Gaussian Noise
Abdullah Arafat Miah, Kaan Icer, Resit Sendag +1
Backdoor attacks pose a significant threat when using third-party data for deep learning development. In these attacks, data can be manipulated to cause a trained model to behave i…