research
∙
08/14/2023
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
Multimodal contrastive learning aims to train a general-purpose feature ...
share
research
∙
08/07/2023
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning
Federated learning (FL) is a nascent distributed learning paradigm to tr...
research
∙
07/15/2023
Why Does Little Robustness Help? Understanding Adversarial Transferability From Surrogate Training
Adversarial examples (AEs) for DNNs have been shown to be transferable: ...
research
∙
03/27/2023
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
Deep neural networks are proven to be vulnerable to backdoor attacks. De...
research
∙
11/22/2022
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples
Point cloud completion, as the upstream procedure of 3D recognition and ...
research
∙
10/04/2022
Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints
Federated learning is a newly emerging distributed learning framework th...
research
∙
03/07/2022
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer
While deep face recognition (FR) systems have shown amazing performance ...
research
∙
09/03/2021
New efficient time-stepping schemes for the anisotropic phase-field dendritic crystal growth model
In this paper, we propose and analyze a first-order and a second-order t...
research
∙
02/22/2020
Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions
Neural networks provide better prediction performance than previous tech...
research
∙
09/20/2019
Augmenting Encrypted Search: A Decentralized Service Realization with Enforced Execution
Searchable symmetric encryption (SSE) allows the data owner to outsource...
research
∙
08/09/2014
