33 citations · 82 across the 10 of their papers we have counts for
10 papers
Exploring the Adversarial Robustness of Face Forgery Detection with Decision-based Black-box Attacks
Zhaoyu Chen, Bo Li, Kaixun Jiang +3
Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and id…
Query-Efficient Decision-based Black-Box Patch Attack
Zhaoyu Chen, Bo Li, Shuang Wu +2
Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce…
Towards Generalizable Data Protection With Transferable Unlearnable Examples
Bin Fang, Bo Li, Shuang Wu +4
Artificial Intelligence (AI) is making a profound impact in almost every domain. One of the crucial factors contributing to this success has been the access to an abundance of high…
Re-thinking Data Availablity Attacks Against Deep Neural Networks
Bin Fang, Bo Li, Shuang Wu +3
The unauthorized use of personal data for commercial purposes and the clandestine acquisition of private data for training machine learning models continue to raise concerns. In re…
Content-based Unrestricted Adversarial Attack
Zhaoyu Chen, Bo Li, Shuang Wu +3
Unrestricted adversarial attacks typically manipulate the semantic content of an image (e.g., color or texture) to create adversarial examples that are both effective and photoreal…
Delving into the Adversarial Robustness of Federated Learning
Jie Zhang, Bo Li, Chen Chen +4
In Federated Learning (FL), models are as fragile as centrally trained models against adversarial examples. However, the adversarial robustness of federated learning remains largel…
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing
Tianyi Zheng
The face anti-spoofing (FAS) method performs well under intra-domain setups. However, its cross-domain performance is unsatisfactory. As a result, the domain generalization (DG) me…
Federated Learning with Label Distribution Skew via Logits Calibration
Jie Zhang, Zhiqi Li, Bo Li +4
Traditional federated optimization methods perform poorly with heterogeneous data (ie, accuracy reduction), especially for highly skewed data. In this paper, we investigate the lab…
Towards Practical Certifiable Patch Defense with Vision Transformer
Zhaoyu Chen, Bo Li, Jianghe Xu +3
Patch attacks, one of the most threatening forms of physical attack in adversarial examples, can lead networks to induce misclassification by modifying pixels arbitrarily in a cont…
DENSE: Data-Free One-Shot Federated Learning
Jie Zhang, Chen Chen, Bo Li +5
One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round. Despite the low com…