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Found 12 papers, 0 papers with code
Parrot-Trained Adversarial Examples: Pushing the Practicality of Black-Box Audio Attacks against Speaker Recognition Models
Motivated by recent advancements in voice conversion (VC), we propose to use the one short sentence knowledge to generate more synthetic speech samples that sound like the target speaker, called parrot speech.
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception
In this work, we formulate the adversarial attack against music signals as a new perception-aware attack framework, which integrates human study into adversarial attack design.
Generalized Federated Learning via Sharpness Aware Minimization
Therefore, in this paper, we revisit the solutions to the distribution shift problem in FL with a focus on local learning generality.
IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification
In this research, we propose a novel attack strategy named IoTGAN to manipulate an IoT device's traffic such that it can evade machine learning based IoT device identification.
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network.
FedLGA: Towards System-Heterogeneity of Federated Learning via Local Gradient Approximation
Federated Learning (FL) is a decentralized machine learning architecture, which leverages a large number of remote devices to learn a joint model with distributed training data.
Context-Aware Online Client Selection for Hierarchical Federated Learning
In addition, client selection for HFL faces more challenges than conventional FL, e. g., the time-varying connection of client-ES pairs and the limited budget of the Network Operator (NO).
Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients
Federated learning (FL) is a new machine learning framework which trains a joint model across a large amount of decentralized computing devices.
Adversarial Machine Learning based Partial-model Attack in IoT
In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices.
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.
When Attackers Meet AI: Learning-empowered Attacks in Cooperative Spectrum Sensing
In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center.
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward
In this paper, we study the stochastic contextual combinatorial multi-armed bandit (CC-MAB) framework that is tailored for volatile arms and submodular reward functions.
