1.8k citations · 2.2k across the 20 of their papers we have counts for
13 papers · 1 filter
Federated Two-stage Learning with Sign-based Voting
Zichen Ma, Zihan Lu, Yu Lu +3
Federated learning is a distributed machine learning mechanism where local devices collaboratively train a shared global model under the orchestration of a central server, while ke…
On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey +3
Improving the robustness of deep neural networks (DNNs) to adversarial examples is an important yet challenging problem for secure deep learning. Across existing defense techniques…
How and When Adversarial Robustness Transfers in Knowledge Distillation?
Rulin Shao, Jinfeng Yi, Pin-Yu Chen +1
Knowledge distillation (KD) has been widely used in teacher-student training, with applications to model compression in resource-constrained deep learning. Current works mainly foc…
Adversarial Attack across Datasets
Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi +2
Existing transfer attack methods commonly assume that the attacker knows the training set (e.g., the label set, the input size) of the black-box victim models, which is usually unr…
Trustworthy AI: From Principles to Practices
Bo Li, Peng Qi, Bo Liu +5
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment of various systems based on it. However, many current AI systems are found vulnerable to…
Training Meta-Surrogate Model for Transferable Adversarial Attack
Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi +1
We consider adversarial attacks to a black-box model when no queries are allowed. In this setting, many methods directly attack surrogate models and transfer the obtained adversari…