26 citations · 46 across the 8 of their papers we have counts for
8 papers
Generalizable Black-Box Adversarial Attack with Meta Learning
Fei Yin, Yong Zhang, Baoyuan Wu +4
In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedb…
Multi-Scale Architectures Matter: On the Adversarial Robustness of Flow-based Lossless Compression
Yi-chong Xia, Bin Chen, Yan Feng +1
As a probabilistic modeling technique, the flow-based model has demonstrated remarkable potential in the field of lossless compression \cite{idf,idf++,lbb,ivpf,iflow},. Compared wi…
sqSGD: Locally Private and Communication Efficient Federated Learning
Yan Feng, Tao Xiong, Ruofan Wu +2
Federated learning (FL) is a technique that trains machine learning models from decentralized data sources. We study FL under local notions of privacy constraints, which provides s…
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches
Nan Ji, YanFei Feng, Haidong Xie +2
The security of object detection systems has attracted increasing attention, especially when facing adversarial patch attacks. Since patch attacks change the pixels in a restricted…
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
Yan Feng, Baoyuan Wu, Yanbo Fan +3
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while…
Toward Adversarial Robustness via Semi-supervised Robust Training
Yiming Li, Baoyuan Wu, Yan Feng +4
Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) thro…
Adversarial Attack on Deep Product Quantization Network for Image Retrieval
Yan Feng, Bin Chen, Tao Dai +1
Deep product quantization network (DPQN) has recently received much attention in fast image retrieval tasks due to its efficiency of encoding high-dimensional visual features espec…
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning
Xue Yang, Yan Feng, Weijun Fang +4
Although federated learning improves privacy of training data by exchanging local gradients or parameters rather than raw data, the adversary still can leverage local gradients and…