12 citations · 23 across the 6 of their papers we have counts for
6 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…
PEEL: A Provable Removal Attack on Deep Hiding
Tao Xiang, Hangcheng Liu, Shangwei Guo +1
Deep hiding, embedding images into another using deep neural networks, has shown its great power in increasing the message capacity and robustness. In this paper, we conduct an in-…
Local Black-box Adversarial Attacks: A Query Efficient Approach
Tao Xiang, Hangcheng Liu, Shangwei Guo +2
Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting wi…
Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models
Shangwei Guo, Tianwei Zhang, Han Qiu +3
Watermarking has become the tendency in protecting the intellectual property of DNN models. Recent works, from the adversary's perspective, attempted to subvert watermarking mechan…
Topology-aware Differential Privacy for Decentralized Image Classification
Shangwei Guo, Tianwei Zhang, Guowen Xu +3
In this paper, we design Top-DP, a novel solution to optimize the differential privacy protection of decentralized image classification systems. The key insight of our solution is…
Byzantine-resilient Decentralized Stochastic Gradient Descent
Shangwei Guo, Tianwei Zhang, Han Yu +4
Decentralized learning has gained great popularity to improve learning efficiency and preserve data privacy. Each computing node makes equal contribution to collaboratively learn a…