4 papers
(A)iSpy: Parasitic Trojans for Machine Learning Infrastructure
Habibur Rahaman, Qipan Xu, Zafaryab Haider +3
Modern machine learning (ML) pipelines depend heavily on third party libraries for graph compilation and hardware acceleration. While current practices audit data and model artifac…
DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples
Abdullah Al Nomaan Nafi, Habibur Rahaman, Zafaryab Haider +4
Numerous techniques have been proposed for generating adversarial examples in white-box settings under strict Lp-norm constraints. However, such norm-bounded examples often fail to…
Secure and Storage-Efficient Deep Learning Models for Edge AI Using Automatic Weight Generation
Habibur Rahaman, Atri Chatterjee, Swarup Bhunia
Complex neural networks require substantial memory to store a large number of synaptic weights. This work introduces WINGs (Automatic Weight Generator for Secure and Storage-Effici…
Runtime Detection of Adversarial Attacks in AI Accelerators Using Performance Counters
Habibur Rahaman, Atri Chatterjee, Swarup Bhunia
Rapid adoption of AI technologies raises several major security concerns, including the risks of adversarial perturbations, which threaten the confidentiality and integrity of AI a…