Search Results for author: Xiaoyong Yuan

Found 17 papers, 2 papers with code

BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection

no code implementations6 May 2024 Saket S. Chaturvedi, Lan Zhang, Wenbin Zhang, Pan He, Xiaoyong Yuan

To tackle this issue, we propose an innovative 2D-oriented backdoor attack against LiDAR-camera fusion methods for 3D object detection, named BadFusion, for preserving trigger effectiveness throughout the entire fusion process.

3D Object Detection Autonomous Driving +3

A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation

no code implementations11 Mar 2024 Pan He, Quanyi Li, Xiaoyong Yuan, Bolei Zhou

Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions.


PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks

no code implementations20 Jul 2023 Shiwei Ding, Lan Zhang, Miao Pan, Xiaoyong Yuan

Collaborative inference has been a promising solution to enable resource-constrained edge devices to perform inference using state-of-the-art deep neural networks (DNNs).

Collaborative Inference Vehicle Re-Identification

Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning

no code implementations10 Jul 2023 Gaurav Bagwe, Xiaoyong Yuan, Miao Pan, Lan Zhang

Federated continual learning (FCL) learns incremental tasks over time from confidential datasets distributed across clients.

Continual Learning

Distributed Pruning Towards Tiny Neural Networks in Federated Learning

no code implementations5 Dec 2022 Hong Huang, Lan Zhang, Chaoyue Sun, Ruogu Fang, Xiaoyong Yuan, Dapeng Wu

To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory- and computing-constrained devices.

Federated Learning Network Pruning

Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning

no code implementations21 Jul 2022 Gaurav Bagwe, Jian Li, Xiaoyong Yuan, Lan Zhang

Moreover, to improve data efficiency and provide better generalization performance, we train the policy model with augmented data (e. g., noisy BSM and noisy surveillance images).

Autonomous Driving reinforcement-learning +1

Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection

no code implementations22 Apr 2022 Saket S. Chaturvedi, Lan Zhang, Xiaoyong Yuan

Specifically, GLA integrates an early-stage fusion via a local attention network and a late-stage fusion via a global attention network to deal with both local and global information, which automatically allocates higher weights to the modality with better detection features at the late-stage fusion to cope with the specific weather condition adaptively.

object-detection Object Detection

Membership Inference Attacks and Defenses in Neural Network Pruning

1 code implementation7 Feb 2022 Xiaoyong Yuan, Lan Zhang

We first explore the impact of neural network pruning on prediction divergence, where the pruning process disproportionately affects the pruned model's behavior for members and non-members.

Inference Attack Membership Inference Attack +2

FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models

no code implementations8 Sep 2021 Lan Zhang, Dapeng Wu, Xiaoyong Yuan

To achieve knowledge transfer across these heterogeneous on-device models, a zero-shot distillation approach is designed without any prerequisites for private on-device data, which is contrary to certain prior research based on a public dataset or a pre-trained data generator.

Federated Learning Transfer Learning

A Vertical Federated Learning Framework for Horizontally Partitioned Labels

no code implementations18 Jun 2021 Wensheng Xia, Ying Li, Lan Zhang, Zhonghai Wu, Xiaoyong Yuan

To address these challenges, we propose a novel vertical federated learning framework named Cascade Vertical Federated Learning (CVFL) to fully utilize all horizontally partitioned labels to train neural networks with privacy-preservation.

Vertical Federated Learning

ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles

no code implementations21 Sep 2020 Xiaoyong Yuan, Leah Ding, Lan Zhang, Xiaolin Li, Dapeng Wu

The experimental results reveal the severity of ES Attack: i) ES Attack successfully steals the victim model without data hurdles, and ES Attack even outperforms most existing model stealing attacks using auxiliary data in terms of model accuracy; ii) most countermeasures are ineffective in defending ES Attack; iii) ES Attack facilitates further attacks relying on the stolen model.

BIG-bench Machine Learning

Generalized Batch Normalization: Towards Accelerating Deep Neural Networks

no code implementations8 Dec 2018 Xiaoyong Yuan, Zheng Feng, Matthew Norton, Xiaolin Li

Utilizing recently introduced concepts from statistics and quantitative risk management, we present a general variant of Batch Normalization (BN) that offers accelerated convergence of Neural Network training compared to conventional BN.


Adaptive Adversarial Attack on Scene Text Recognition

no code implementations9 Jul 2018 Xiaoyong Yuan, Pan He, Xiaolin Andy Li, Dapeng Oliver Wu

We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e. g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks.

Adversarial Attack Image Classification +3

Adversarial Examples: Attacks and Defenses for Deep Learning

1 code implementation19 Dec 2017 Xiaoyong Yuan, Pan He, Qile Zhu, Xiaolin Li

In this paper, we review recent findings on adversarial examples for deep neural networks, summarize the methods for generating adversarial examples, and propose a taxonomy of these methods.

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

no code implementations4 Dec 2017 Ruimin Sun, Xiaoyong Yuan, Pan He, Qile Zhu, Aokun Chen, Andre Gregio, Daniela Oliveira, Xiaolin Li

Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead.

General Classification Malware Detection

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