activity
20202022
most citedSecuring Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters

35 citations · 59 across the 7 of their papers we have counts for

collaborators

9 papers

cs.CR20224 cited

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…

cs.CR20221 cited

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…

cs.CR20221 cited

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…

cs.CR20217 cited

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…

cs.CR202111 cited

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…

cs.CR2021

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…