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20212023
most citedDENSE: Data-Free One-Shot Federated Learning

33 citations · 82 across the 10 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CV2023

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…

cs.CV2023★ 2 cited

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…

cs.CR2023★ 4 cited

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…

cs.CR2023★ 2 cited

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…

cs.CV2023★ 10 cited

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…

cs.LG2023★ 4 cited

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…

cs.CV2023★ 3 cited

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…