46 citations · 150 across the 32 of their papers we have counts for
6 papers · 1 filter
Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study
Ayana Moshruba, Ihsen Alouani, Maryam Parsa
While machine learning (ML) models are becoming mainstream, especially in sensitive application areas, the risk of data leakage has become a growing concern. Attacks like membershi…
SnatchML: Hijacking ML models without Training Access
Mahmoud Ghorbel, Halima Bouzidi, Ioan Marius Bilasco +1
Model hijacking can cause significant accountability and security risks since the owner of a hijacked model can be framed for having their model offer illegal or unethical services…
Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks
Hamed Poursiami, Ihsen Alouani, Maryam Parsa
As spiking neural networks (SNNs) gain traction in deploying neuromorphic computing solutions, protecting their intellectual property (IP) has become crucial. Without adequate safe…
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications
Amira Guesmi, Muhammad Abdullah Hanif, Ihsen Alouani +2
Monocular depth estimation (MDE) has advanced significantly, primarily through the integration of convolutional neural networks (CNNs) and more recently, Transformers. However, con…
BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks
Hamed Poursiami, Ihsen Alouani, Maryam Parsa
With the mainstream integration of machine learning into security-sensitive domains such as healthcare and finance, concerns about data privacy have intensified. Conventional artif…
Evasive Hardware Trojan through Adversarial Power Trace
Behnam Omidi, Khaled N. Khasawneh, Ihsen Alouani
The globalization of the Integrated Circuit (IC) supply chain, driven by time-to-market and cost considerations, has made ICs vulnerable to hardware Trojans (HTs). Against this thr…