46 citations · 150 across the 32 of their papers we have counts for
33 papers
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…
Stealth by Conformity: Evading Robust Aggregation through Adaptive Poisoning
Ryan McGaughey, Jesus Martinez del Rincon, Ihsen Alouani
Federated Learning (FL) is a distributed learning paradigm designed to address privacy concerns. However, FL is vulnerable to poisoning attacks, where Byzantine clients compromise…
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…
On Jailbreaking Quantized Language Models Through Fault Injection Attacks
Noureldin Zahran, Ahmad Tahmasivand, Ihsen Alouani +2
The safety alignment of Language Models (LMs) is a critical concern, yet their integrity can be challenged by direct parameter manipulation attacks, such as those potentially induc…
Input-Triggered Hardware Trojan Attack on Spiking Neural Networks
Spyridon Raptis, Paul Kling, Ioannis Kaskampas +2
Neuromorphic computing based on spiking neural networks (SNNs) is emerging as a promising alternative to traditional artificial neural networks (ANNs), offering unique advantages i…
Attention Eclipse: Manipulating Attention to Bypass LLM Safety-Alignment
Pedram Zaree, Md Abdullah Al Mamun, Quazi Mishkatul Alam +3
Recent research has shown that carefully crafted jailbreak inputs can induce large language models to produce harmful outputs, despite safety measures such as alignment. It is impo…
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…
Attention Deficit is Ordered! Fooling Deformable Vision Transformers with Collaborative Adversarial Patches
Quazi Mishkatul Alam, Bilel Tarchoun, Ihsen Alouani +1
The latest generation of transformer-based vision models has proven to be superior to Convolutional Neural Network (CNN)-based models across several vision tasks, largely attribute…
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…
Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems
Bilel Tarchoun, Quazi Mishkatul Alam, Nael Abu-Ghazaleh +1
Adversarial patches exemplify the tangible manifestation of the threat posed by adversarial attacks on Machine Learning (ML) models in real-world scenarios. Robustness against thes…
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…
DAP: A Dynamic Adversarial Patch for Evading Person Detectors
Amira Guesmi, Ruitian Ding, Muhammad Abdullah Hanif +2
Patch-based adversarial attacks were proven to compromise the robustness and reliability of computer vision systems. However, their conspicuous and easily detectable nature challen…
Jedi: Entropy-based Localization and Removal of Adversarial Patches
Bilel Tarchoun, Anouar Ben Khalifa, Mohamed Ali Mahjoub +2
Real-world adversarial physical patches were shown to be successful in compromising state-of-the-art models in a variety of computer vision applications. Existing defenses that are…
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…
AdvART: Adversarial Art for Camouflaged Object Detection Attacks
Amira Guesmi, Ioan Marius Bilasco, Muhammad Shafique +1
Physical adversarial attacks pose a significant practical threat as it deceives deep learning systems operating in the real world by producing prominent and maliciously designed ph…
APARATE: Adaptive Adversarial Patch for CNN-based Monocular Depth Estimation for Autonomous Navigation
Amira Guesmi, Muhammad Abdullah Hanif, Ihsen Alouani +1
In recent times, monocular depth estimation (MDE) has experienced significant advancements in performance, largely attributed to the integration of innovative architectures, i.e.,…
Adversarial Attack on Radar-based Environment Perception Systems
Amira Guesmi, Ihsen Alouani
Due to their robustness to degraded capturing conditions, radars are widely used for environment perception, which is a critical task in applications like autonomous vehicles. More…
Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks
Amira Guesmi, Ihsen Alouani, Khaled N. Khasawneh +4
Machine-learning architectures, such as Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks: inputs crafted carefully to force the system output to a wrong l…