Deep learning for iris recognition: A survey
K Nguyen, H Proença, F Alonso-Fernandez - ACM Computing Surveys, 2024 - dl.acm.org
In this survey, we provide a comprehensive review of more than 200 articles, technical
reports, and GitHub repositories published over the last 10 years on the recent …
reports, and GitHub repositories published over the last 10 years on the recent …
[HTML][HTML] Poisoning attacks and countermeasures in intelligent networks: Status quo and prospects
Over the past years, the emergence of intelligent networks empowered by machine learning
techniques has brought great facilitates to different aspects of human life. However, using …
techniques has brought great facilitates to different aspects of human life. However, using …
Blind backdoors in deep learning models
E Bagdasaryan, V Shmatikov - 30th USENIX Security Symposium …, 2021 - usenix.org
We investigate a new method for injecting backdoors into machine learning models, based
on compromising the loss-value computation in the model-training code. We use it to …
on compromising the loss-value computation in the model-training code. We use it to …
Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead
Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …
(DL) is already present in many applications ranging from computer vision for medicine to …
Robust machine learning systems: Challenges, current trends, perspectives, and the road ahead
Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …
Deep learning for edge computing: Current trends, cross-layer optimizations, and open research challenges
In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …
their unmatchable performance in several applications, such as image processing, computer …
Towards energy-efficient and secure edge AI: A cross-layer framework ICCAD special session paper
The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …
processed on regular basis has pushed processing to the edge of the computing systems …
P3: Privacy-Preserving Scheme Against Poisoning Attacks in Mobile-Edge Computing
P Zhao, H Huang, X Zhao… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Mobile-edge computing (MEC) has emerged to enable users to offload their location data
into the MEC server, and at the same time, the MEC server executes the location-aware data …
into the MEC server, and at the same time, the MEC server executes the location-aware data …
Qusecnets: Quantization-based defense mechanism for securing deep neural network against adversarial attacks
Adversarial examples have emerged as a significant threat to machine learning algorithms,
especially to the convolutional neural networks (CNNs). In this paper, we propose two …
especially to the convolutional neural networks (CNNs). In this paper, we propose two …
Building robust machine learning systems: Current progress, research challenges, and opportunities
Machine learning, in particular deep learning, is being used in almost all the aspects of life
to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications …
to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications …