10 citations · 31 across the 6 of their papers we have counts for
7 papers
Temporal Gradient Inversion Attacks with Robust Optimization
Bowen Li, Hanlin Gu, Ruoxin Chen +5
Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged…
Adversarial Representation Sharing: A Quantitative and Secure Collaborative Learning Framework
Jikun Chen, Feng Qiang, Na Ruan
The performance of deep learning models highly depends on the amount of training data. It is common practice for today's data holders to merge their datasets and train models colla…
Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features
Zekun Sun, Yujie Han, Zeyu Hua +2
Deepfakes is a branch of malicious techniques that transplant a target face to the original one in videos, resulting in serious problems such as infringement of copyright, confusio…
Label Smoothing and Adversarial Robustness
Chaohao Fu, Hongbin Chen, Na Ruan +1
Recent studies indicate that current adversarial attack methods are flawed and easy to fail when encountering some deliberately designed defense. Sometimes even a slight modificati…
FraudJudger: Real-World Data Oriented Fraud Detection on Digital Payment Platforms
Ruoyu Deng, Na Ruan
Automated fraud behaviors detection on electronic payment platforms is a tough problem. Fraud users often exploit the vulnerability of payment platforms and the carelessness of use…
Catfish Effect Between Internal and External Attackers:Being Semi-honest is Helpful
Hanqing Liu, Na Ruan, Joseph K. Liu
The consensus protocol named proof of work (PoW) is widely applied by cryptocurrencies like Bitcoin. Although security of a PoW cryptocurrency is always the top priority, it is thr…