Search Results for author: Yingqi Liu

Found 23 papers, 13 papers with code

Decentralized Directed Collaboration for Personalized Federated Learning

no code implementations CVPR 2024 Yingqi Liu, Yifan Shi, Qinglun Li, Baoyuan Wu, Xueqian Wang, Li Shen

To avoid the central failure and communication bottleneck in the server-based FL, we concentrate on the Decentralized Personalized Federated Learning (DPFL) that performs distributed model training in a Peer-to-Peer (P2P) manner.

Personalized Federated Learning

Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training

no code implementations24 May 2023 Yifan Shi, Yingqi Liu, Yan Sun, Zihao Lin, Li Shen, Xueqian Wang, DaCheng Tao

Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems.

Personalized Federated Learning

Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy

1 code implementation1 May 2023 Yifan Shi, Kang Wei, Li Shen, Yingqi Liu, Xueqian Wang, Bo Yuan, DaCheng Tao

To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise.

Federated Learning

Detecting Backdoors in Pre-trained Encoders

1 code implementation CVPR 2023 Shiwei Feng, Guanhong Tao, Siyuan Cheng, Guangyu Shen, Xiangzhe Xu, Yingqi Liu, Kaiyuan Zhang, Shiqing Ma, Xiangyu Zhang

We show the effectiveness of our method on image encoders pre-trained on ImageNet and OpenAI's CLIP 400 million image-text pairs.

Self-Supervised Learning

Make Landscape Flatter in Differentially Private Federated Learning

1 code implementation CVPR 2023 Yifan Shi, Yingqi Liu, Kang Wei, Li Shen, Xueqian Wang, DaCheng Tao

Specifically, DP-FedSAM integrates Sharpness Aware Minimization (SAM) optimizer to generate local flatness models with better stability and weight perturbation robustness, which results in the small norm of local updates and robustness to DP noise, thereby improving the performance.

Federated Learning

BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense

1 code implementation16 Jan 2023 Siyuan Cheng, Guanhong Tao, Yingqi Liu, Shengwei An, Xiangzhe Xu, Shiwei Feng, Guangyu Shen, Kaiyuan Zhang, QiuLing Xu, Shiqing Ma, Xiangyu Zhang

Attack forensics, a critical counter-measure for traditional cyber attacks, is hence of importance for defending model backdoor attacks.

Backdoor Attack

MEDIC: Remove Model Backdoors via Importance Driven Cloning

no code implementations CVPR 2023 QiuLing Xu, Guanhong Tao, Jean Honorio, Yingqi Liu, Shengwei An, Guangyu Shen, Siyuan Cheng, Xiangyu Zhang

It trains the clone model from scratch on a very small subset of samples and aims to minimize a cloning loss that denotes the differences between the activations of important neurons across the two models.

Knowledge Distillation

Backdoor Vulnerabilities in Normally Trained Deep Learning Models

no code implementations29 Nov 2022 Guanhong Tao, Zhenting Wang, Siyuan Cheng, Shiqing Ma, Shengwei An, Yingqi Liu, Guangyu Shen, Zhuo Zhang, Yunshu Mao, Xiangyu Zhang

We leverage 20 different types of injected backdoor attacks in the literature as the guidance and study their correspondences in normally trained models, which we call natural backdoor vulnerabilities.

Data Poisoning

Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps

no code implementations16 Aug 2022 Kun Qiu, Harry Chang, Ying Wang, Xiahui Yu, Wenjun Zhu, Yingqi Liu, Jianwei Ma, Weigang Li, Xiaobo Liu, Shuo Dai

Sophisticated traffic analytics, such as the encrypted traffic analytics and unknown malware detection, emphasizes the need for advanced methods to analyze the network traffic.

Malware Detection Traffic Classification

DECK: Model Hardening for Defending Pervasive Backdoors

no code implementations18 Jun 2022 Guanhong Tao, Yingqi Liu, Siyuan Cheng, Shengwei An, Zhuo Zhang, QiuLing Xu, Guangyu Shen, Xiangyu Zhang

As such, using the samples derived from our attack in adversarial training can harden a model against these backdoor vulnerabilities.

Decoder

Blueprint Separable Residual Network for Efficient Image Super-Resolution

1 code implementation12 May 2022 Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Jinjin Gu, Yu Qiao, Chao Dong

One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation.

Image Super-Resolution

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

2 code implementations11 May 2022 Yawei Li, Kai Zhang, Radu Timofte, Luc van Gool, Fangyuan Kong, Mingxi Li, Songwei Liu, Zongcai Du, Ding Liu, Chenhui Zhou, Jingyi Chen, Qingrui Han, Zheyuan Li, Yingqi Liu, Xiangyu Chen, Haoming Cai, Yu Qiao, Chao Dong, Long Sun, Jinshan Pan, Yi Zhu, Zhikai Zong, Xiaoxiao Liu, Zheng Hui, Tao Yang, Peiran Ren, Xuansong Xie, Xian-Sheng Hua, Yanbo Wang, Xiaozhong Ji, Chuming Lin, Donghao Luo, Ying Tai, Chengjie Wang, Zhizhong Zhang, Yuan Xie, Shen Cheng, Ziwei Luo, Lei Yu, Zhihong Wen, Qi Wu1, Youwei Li, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Yuanfei Huang, Meiguang Jin, Hua Huang, Jing Liu, Xinjian Zhang, Yan Wang, Lingshun Long, Gen Li, Yuanfan Zhang, Zuowei Cao, Lei Sun, Panaetov Alexander, Yucong Wang, Minjie Cai, Li Wang, Lu Tian, Zheyuan Wang, Hongbing Ma, Jie Liu, Chao Chen, Yidong Cai, Jie Tang, Gangshan Wu, Weiran Wang, Shirui Huang, Honglei Lu, Huan Liu, Keyan Wang, Jun Chen, Shi Chen, Yuchun Miao, Zimo Huang, Lefei Zhang, Mustafa Ayazoğlu, Wei Xiong, Chengyi Xiong, Fei Wang, Hao Li, Ruimian Wen, Zhijing Yang, Wenbin Zou, Weixin Zheng, Tian Ye, Yuncheng Zhang, Xiangzhen Kong, Aditya Arora, Syed Waqas Zamir, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Dandan Gaoand Dengwen Zhouand Qian Ning, Jingzhu Tang, Han Huang, YuFei Wang, Zhangheng Peng, Haobo Li, Wenxue Guan, Shenghua Gong, Xin Li, Jun Liu, Wanjun Wang, Dengwen Zhou, Kun Zeng, Hanjiang Lin, Xinyu Chen, Jinsheng Fang

The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.

Image Super-Resolution

Complex Backdoor Detection by Symmetric Feature Differencing

1 code implementation CVPR 2022 Yingqi Liu, Guangyu Shen, Guanhong Tao, Zhenting Wang, Shiqing Ma, Xiangyu Zhang

Our results on the TrojAI competition rounds 2-4, which have patch backdoors and filter backdoors, show that existing scanners may produce hundreds of false positives (i. e., clean models recognized as trojaned), while our technique removes 78-100% of them with a small increase of false negatives by 0-30%, leading to 17-41% overall accuracy improvement.

Backdoor Scanning for Deep Neural Networks through K-Arm Optimization

1 code implementation9 Feb 2021 Guangyu Shen, Yingqi Liu, Guanhong Tao, Shengwei An, QiuLing Xu, Siyuan Cheng, Shiqing Ma, Xiangyu Zhang

By iteratively and stochastically selecting the most promising labels for optimization with the guidance of an objective function, we substantially reduce the complexity, allowing to handle models with many classes.

Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification

2 code implementations21 Dec 2020 Siyuan Cheng, Yingqi Liu, Shiqing Ma, Xiangyu Zhang

Trojan (backdoor) attack is a form of adversarial attack on deep neural networks where the attacker provides victims with a model trained/retrained on malicious data.

Backdoor Attack

Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples

1 code implementation NeurIPS 2018 Guanhong Tao, Shiqing Ma, Yingqi Liu, Xiangyu Zhang

Results show that our technique can achieve 94% detection accuracy for 7 different kinds of attacks with 9. 91% false positives on benign inputs.

Attribute Face Recognition +1

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