6 citations · 6 across the 7 of their papers we have counts for
7 papers
Fuzziness-tuned: Improving the Transferability of Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considera…
Improving the Transferability of Adversarial Examples via Direction Tuning
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
In the transfer-based adversarial attacks, adversarial examples are only generated by the surrogate models and achieve effective perturbation in the victim models. Although conside…
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model aga…
FACM: Intermediate Layer Still Retain Effective Features against Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
In strong adversarial attacks against deep neural networks (DNN), the generated adversarial example will mislead the DNN-implemented classifier by destroying the output features of…
Gradient Aligned Attacks via a Few Queries
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
Black-box query attacks, which rely only on the output of the victim model, have proven to be effective in attacking deep learning models. However, existing black-box query attacks…
Latent Dirichlet Allocation Model Training with Differential Privacy
Fangyuan Zhao, Xuebin Ren, Shusen Yang +3
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various app…
Energy-efficient Analytics for Geographically Distributed Big Data
Peng Zhao, Shusen Yang, Xinyu Yang +2
Big data analytics on geographically distributed datasets (across data centers or clusters) has been attracting increasing interests from both academia and industry, but also signi…