46 citations · 150 across the 32 of their papers we have counts for
8 papers · 1 filter
Faults That Fortify: CNN Adversarial Robustness via GPU Undervolting
Behnam Omidi, Ahmad Tahmasivand, Husam Alsyouri +4
Convolutional Neural Networks (CNNs) face a dual challenge: vulnerability to adversarial attacks and prohibitive training cost. Adversarial training is effective but expensive, a b…
Privacy in Federated Learning with Spiking Neural Networks
Dogukan Aksu, Jesus Martinez del Rincon, Ihsen Alouani
Spiking neural networks (SNNs) have emerged as prominent candidates for embedded and edge AI. Their inherent low power consumption makes them far more efficient than conventional A…
Poison Once, Refuse Forever: Weaponizing Alignment for Injecting Bias in LLMs
Md Abdullah Al Mamun, Ihsen Alouani, Nael Abu-Ghazaleh
Large Language Models (LLMs) are aligned to meet ethical standards and safety requirements by training them to refuse answering harmful or unsafe prompts. In this paper, we demonst…
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…
May the Noise be with you: Adversarial Training without Adversarial Examples
Ayoub Arous, Andres F Lopez-Lopera, Nael Abu-Ghazaleh +1
In this paper, we investigate the following question: Can we obtain adversarially-trained models without training on adversarial examples? Our intuition is that training a model wi…
Co(ve)rtex: ML Models as storage channels and their (mis-)applications
Md Abdullah Al Mamun, Quazi Mishkatul Alam, Erfan Shayegani +3
Machine learning (ML) models are overparameterized to support generality and avoid overfitting. The state of these parameters is essentially a "don't-care" with respect to the prim…
Exploring Machine Learning Privacy/Utility trade-off from a hyperparameters Lens
Ayoub Arous, Amira Guesmi, Muhammad Abdullah Hanif +2
Machine Learning (ML) architectures have been applied to several applications that involve sensitive data, where a guarantee of users' data privacy is required. Differentially Priv…
Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters
Rida El-Allami, Alberto Marchisio, Muhammad Shafique +1
Deep Learning (DL) algorithms have gained popularity owing to their practical problem-solving capacity. However, they suffer from a serious integrity threat, i.e., their vulnerabil…