46 citations · 150 across the 32 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints
Amira Guesmi, Khaled N. Khasawneh, Nael Abu-Ghazaleh +1
Advances in deep learning have enabled a wide range of promising applications. However, these systems are vulnerable to Adversarial Machine Learning (AML) attacks; adversarially cr…
Adversarial Attacks in a Multi-view Setting: An Empirical Study of the Adversarial Patches Inter-view Transferability
Bilel Tarchoun, Ihsen Alouani, Anouar Ben Khalifa +1
While machine learning applications are getting mainstream owing to a demonstrated efficiency in solving complex problems, they suffer from inherent vulnerability to adversarial at…
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks
Nicolas Fleury, Theo Dubrunquez, Ihsen Alouani
In the recent years, Portable Document Format, commonly known as PDF, has become a democratized standard for document exchange and dissemination. This trend has been due to its cha…
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling
Md Shohidul Islam, Ihsen Alouani, Khaled N. Khasawneh
Machine learning-based hardware malware detectors (HMDs) offer a potential game changing advantage in defending systems against malware. However, HMDs suffer from adversarial attac…
Defensive Approximation: Securing CNNs using Approximate Computing
Amira Guesmi, Ihsen Alouani, Khaled Khasawneh +4
In the past few years, an increasing number of machine-learning and deep learning structures, such as Convolutional Neural Networks (CNNs), have been applied to solving a wide rang…
NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips
Valerio Venceslai, Alberto Marchisio, Ihsen Alouani +2
Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a…