9 citations · 21 across the 7 of their papers we have counts for
7 papers
Erase and Repair: An Efficient Box-Free Removal Attack on High-Capacity Deep Hiding
Hangcheng Liu, Tao Xiang, Shangwei Guo +3
Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In t…
Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Accelerator
Xiaobei Yan, Xiaoxuan Lou, Guowen Xu +4
DNN accelerators have been widely deployed in many scenarios to speed up the inference process and reduce the energy consumption. One big concern about the usage of the accelerator…
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network
Ziheng Huang, Boheng Li, Yan Cai +5
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis. However, well-trained GANs are under the threat of…
Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding
Han Li, Hangcheng Liu, Shangwei Guo +4
Deep hiding, concealing secret information using Deep Neural Networks (DNNs), can significantly increase the embedding rate and improve the efficiency of secret sharing. Existing w…
Privacy-preserving Decentralized Deep Learning with Multiparty Homomorphic Encryption
Guowen Xu, Guanlin Li, Shangwei Guo +2
Decentralized deep learning plays a key role in collaborative model training due to its attractive properties, including tolerating high network latency and less prone to single-po…
Robust and Privacy-Preserving Collaborative Learning: A Comprehensive Survey
Shangwei Guo, Xu Zhang, Fei Yang +4
With the rapid demand of data and computational resources in deep learning systems, a growing number of algorithms to utilize collaborative machine learning techniques, for example…