Search Results for author:
Found 35 papers, 3 papers with code
Deep Supervised Hashing with Triplet Labels
The current state-of-the-art deep hashing method DPSH~\cite{li2015feature}, which is based on pairwise labels, performs image feature learning and hash code learning simultaneously by maximizing the likelihood of pairwise similarities.
Unsupervised Deep Shape Descriptor With Point Distribution Learning
This paper proposes a novel probabilistic framework for the learning of unsupervised deep shape descriptors with point distribution learning.
Identifying Ambiguous Similarity Conditions via Semantic Matching
To this end, we introduce a novel evaluation criterion by predicting the comparison's correctness after assigning the learned embeddings to their optimal conditions, which measures how much WS-CSL could cover latent semantics as the supervised model.
Deep Learning for Launching and Mitigating Wireless Jamming Attacks
An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented.
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls
To mitigate the impact of limited training data, we develop an active learning approach that first builds a classifier based on a small number of API calls and uses this classifier to select samples to further collect their labels.
Spectrum Data Poisoning with Adversarial Deep Learning
We consider the case that a cognitive transmitter senses the spectrum and transmits on idle channels determined by a machine learning algorithm.
Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data
The exploratory attack with limited training data is shown to fail to reliably infer the target classifier of a real text classifier API that is available online to the public.
BIG-bench Machine Learning
Generative Adversarial Network
+1
Generative Adversarial Network for Wireless Signal Spoofing
Building upon deep learning techniques, this paper introduces a spoofing attack by an adversary pair of a transmitter and a receiver that assume the generator and discriminator roles in the GAN and play a minimax game to generate the best spoofing signals that aim to fool the best trained defense mechanism.
IoT Network Security from the Perspective of Adversarial Deep Learning
While there is a surge of interest to understand the security issues of machine learning, their implications have not been understood yet for wireless applications such as those in IoT systems that are susceptible to various attacks due the open and broadcast nature of wireless communications.
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments
Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network.
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks.
Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks
A transmitter applies deep learning on its spectrum sensing results to predict idle time slots for data transmission.
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium.
Deep Learning for Wireless Communications
Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom.
Unsupervised Learning of Global Registration of Temporal Sequence of Point Clouds
Global registration of point clouds aims to find an optimal alignment of a sequence of 2D or 3D point sets.
Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving
Our model builds on a state-of-the-art Bird's-Eye View (BEV) network that fuses voxelized features from a sequence of historical LiDAR data as well as rasterized high-definition map to perform detection and prediction tasks.
Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing.
Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers
As machine learning (ML) algorithms are used to process wireless signals to make decisions such as PHY-layer authentication, the training data characteristics (e. g., device-level information) and the environment conditions (e. g., channel information) under which the data is collected may leak to the ML model.
Adversarial Machine Learning for 5G Communications Security
For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station.
How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning
In this paper, reinforcement learning (RL) for network slicing is considered in NextG radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time.
Networking and Internet Architecture
Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing
We show that the portion of the reward achieved by real requests may be much less than the reward that would be achieved when there was no attack.
Polyphone Disambiguition in Mandarin Chinese with Semi-Supervised Learning
In this paper, we propose a novel semi-supervised learning (SSL) framework for Mandarin Chinese polyphone disambiguation that can potentially leverage unlimited unlabeled text data.
Improve GAN-based Neural Vocoder using Pointwise Relativistic LeastSquare GAN
GAN-based neural vocoders, such as Parallel WaveGAN and MelGAN have attracted great interest due to their lightweight and parallel structures, enabling them to generate high fidelity waveform in a real-time manner.
Membership Inference Attack and Defense for Wireless Signal Classifiers with Deep Learning
An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier.
Adversarial Attacks against Deep Learning Based Power Control in Wireless Communications
The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output.
A novel framework to quantify uncertainty in peptide-tandem mass spectrum matches with application to nanobody peptide identification
To address these issues, we then develop a novel framework and method that treats peptide-spectrum matching as a Bayesian model selection problem with an incomplete model space, which are, to our knowledge, the first to account for all sources of PSM error without relying on the aforementioned assumptions.
C$^2$SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction
Although compressive sensing (CS) can be adopted to compress the signals to reduce communication bandwidth requirement, it needs a complex reconstruction procedure before the signal can be used for seizure prediction.
Jamming Attacks on Federated Learning in Wireless Networks
Federated learning (FL) offers a decentralized learning environment so that a group of clients can collaborate to train a global model at the server, while keeping their training data confidential.
Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks
In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a DNN for signal identification.
Rethinking the Detection Head Configuration for Traffic Object Detection
The first is the detection head and object distribution matching strategy, which guides the rational configuration of detection head, so as to leverage multi-scale features to effectively detect objects at vastly different scales.
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing
In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier.
Jamming Attacks on Decentralized Federated Learning in General Multi-Hop Wireless Networks
We show that the DFL performance can be significantly reduced by jamming attacks launched in a wireless network and characterize the attack surface as a vulnerability study before the safe deployment of DFL over wireless networks.
Controllable Motion Diffusion Model
To enable real-time motion synthesis with diffusion models in response to time-varying control signals, we propose the framework of the Controllable Motion Diffusion Model (COMODO).
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction
Unlike existing approaches treating models as black boxes, Scale-MIA recognizes the importance of the intricate architecture and inner workings of machine learning models.
Securing NextG Systems against Poisoning Attacks on Federated Learning: A Game-Theoretic Solution
This paper studies the poisoning attack and defense interactions in a federated learning (FL) system, specifically in the context of wireless signal classification using deep learning for next-generation (NextG) communications.
