Search Results for author:
Found 9 papers, 4 papers with code
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
Our work could provide a useful tool to defend against certain adversarial attacks on deep neural networks.
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
Finally, we demonstrate that the layers $Block4\_conv1$ and $Block5\_cov1$ of the VGG-16 model are more susceptible to adversarial attacks.
TMHOI: Translational Model for Human-Object Interaction Detection
Our method effectively captures the sentiment representation of HOIs by integrating both spatial and semantic knowledge.
Computational Efficiency
Human-Object Interaction Detection
+1
Zero-bias Deep Neural Network for Quickest RF Signal Surveillance
In this paper, we provide a deep learning framework for RF signal surveillance.
Class-Incremental Learning for Wireless Device Identification in IoT
The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services.
Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT
Abnormality detection is essential to the performance of safety-critical and latency-constrained systems.
Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey
In this paper, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators.
Distant Domain Transfer Learning for Medical Imaging
In this paper, we propose a distant domain transfer learning (DDTL) method for medical image classification.
Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices
In this paper, we propose an enhanced deep learning framework for IoT device identification using physical layer signals.
