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Found 9 papers, 1 papers with code
Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations
We demonstrate that the dynamic nature of EvilEye enables attackers to adapt adversarial examples across a variety of objects with a significantly higher ASR compared to state-of-the-art physical world attack frameworks.
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness
Considering the co-importance of model compactness and robustness in practical applications, several prior works have explored to improve the adversarial robustness of the sparse neural networks.
Robot Motion Planning as Video Prediction: A Spatio-Temporal Neural Network-based Motion Planner
Despite the current development in this direction, the efficient capture and processing of important sequential and spatial information, in a direct and simultaneous way, is still relatively under-explored.
CHIP: CHannel Independence-based Pruning for Compact Neural Networks
Filter pruning has been widely used for neural network compression because of its enabled practical acceleration.
Multi-Source Data Fusion for Cyberattack Detection in Power Systems
We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions.
On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification
With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis.
Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness
The agents are involved in capturing the images of the scene using their smartphones (or on-board cameras in case of drones) as directed by the MARL algorithm.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering
Furthermore, since privacy is a concern in collaborative filtering, a privacy-preserving method is proposed to derive HCFContext model parameters based on the concepts of homomorphic encryption.
CollabLoc: Privacy-Preserving Multi-Modal Localization via Collaborative Information Fusion
Mobile phones provide an excellent opportunity for building context-aware applications.
