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09/04/2023
Safe and Robust Watermark Injection with a Single OoD Image
Training a high-performance deep neural network requires large amounts o...
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06/04/2023
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Data-free knowledge distillation (KD) helps transfer knowledge from a pr...
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02/23/2023
On the Hardness of Robustness Transfer: A Perspective from Rademacher Complexity over Symmetric Difference Hypothesis Space
Recent studies demonstrated that the adversarially robust learning under...
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10/23/2022
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
As deep learning blooms with growing demand for computation and data res...
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10/12/2022
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork
Deep neural networks (DNNs) are vulnerable to backdoor attacks. Previous...
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07/04/2022
How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts
Increasing concerns have been raised on deep learning fairness in recent...
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03/18/2022
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
Federated learning (FL) provides a distributed learning framework for mu...
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06/18/2021
Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning
Federated learning (FL) emerges as a popular distributed learning schema...
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05/20/2021
Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Federated Learning (FL) is a decentralized machine-learning paradigm, in...
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01/19/2021
On Dynamic Noise Influence in Differentially Private Learning
Protecting privacy in learning while maintaining the model performance h...
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02/10/2018
