Feshi: Feature map-based stealthy hardware intrinsic attack
Convolutional Neural Networks (CNN) have shown impressive performance in computer
vision, natural language processing, and many other applications, but they exhibit high …
vision, natural language processing, and many other applications, but they exhibit high …
Exploiting vulnerabilities in deep neural networks: Adversarial and fault-injection attacks
F Khalid, MA Hanif, M Shafique - arXiv preprint arXiv:2105.03251, 2021 - arxiv.org
From tiny pacemaker chips to aircraft collision avoidance systems, the state-of-the-art Cyber-
Physical Systems (CPS) have increasingly started to rely on Deep Neural Networks (DNNs) …
Physical Systems (CPS) have increasingly started to rely on Deep Neural Networks (DNNs) …
Resilience of Deep Learning applications: a systematic survey of analysis and hardening techniques
Machine Learning (ML) is currently being exploited in numerous applications being one of
the most effective Artificial Intelligence (AI) technologies, used in diverse fields, such as …
the most effective Artificial Intelligence (AI) technologies, used in diverse fields, such as …
Red-cane: A systematic methodology for resilience analysis and design of capsule networks under approximations
A Marchisio, V Mrazek, MA Hanif… - … Design, Automation & …, 2020 - ieeexplore.ieee.org
Recent advances in Capsule Networks (CapsNets) have shown their superior learning
capability, compared to the traditional Convolutional Neural Networks (CNNs). However, the …
capability, compared to the traditional Convolutional Neural Networks (CNNs). However, the …
Introduction to machine learning for physicians: a survival guide for data deluge
Many modern research fields increasingly rely on collecting and analysing massive, often
unstructured, and unwieldy datasets. Consequently, there is growing interest in machine …
unstructured, and unwieldy datasets. Consequently, there is growing interest in machine …
Joint learning and channel coding for error-tolerant IoT systems based on machine learning
In several machine learning (ML) based Internet of Things (IoT) systems, data are captured
by IoT devices and then transmitted over a wireless channel for remote processing. Since …
by IoT devices and then transmitted over a wireless channel for remote processing. Since …
FasTrCaps: An integrated framework for fast yet accurate training of capsule networks
Recently, Capsule Networks (CapsNets) have shown improved performance compared to
the traditional Convolutional Neural Networks (CNNs), by encoding and preserving spatial …
the traditional Convolutional Neural Networks (CNNs), by encoding and preserving spatial …
Efficient and Responsible Adaptation of Large Language Models for Robust Top-k Recommendations
Conventional recommendation systems (RSs) are typically optimized to enhance
performance metrics uniformly across all training samples. This makes it hard for data-driven …
performance metrics uniformly across all training samples. This makes it hard for data-driven …
Adversarial ML for DNNs, CapsNets, and SNNs at the Edge
A Marchisio, MA Hanif, M Shafique - … Learning for Cyber-Physical, IoT, and …, 2023 - Springer
Recent studies have shown that Machine Learning (ML) algorithm suffers from several
vulnerability threats. Among them, adversarial attacks represent one of the most critical …
vulnerability threats. Among them, adversarial attacks represent one of the most critical …
Labani: Layer-based noise injection attack on convolutional neural networks
Hardware accelerator-based CNN inference improves the performance and latency but
increases the time-to-market. As a result, CNN deployment on hardware is often outsourced …
increases the time-to-market. As a result, CNN deployment on hardware is often outsourced …