46 citations · 133 across the 34 of their papers we have counts for
3 papers · 1 filter
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…
Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU Systems
Sankha Baran Dutta, Hoda Naghibijouybari, Arjun Gupta +3
The deep learning revolution has been enabled in large part by GPUs, and more recently accelerators, which make it possible to carry out computationally demanding training and infe…
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…