15 citations · 25 across the 3 of their papers we have counts for
7 papers
FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques
Han Qiu, Yi Zeng, Tianwei Zhang +2
It is extensively studied that Deep Neural Networks (DNNs) are vulnerable to Adversarial Examples (AEs). With more and more advanced adversarial attack methods have been developed,…
When Machine Learning Meets Congestion Control: A Survey and Comparison
Huiling Jiang, Qing Li, Yong Jiang +4
Machine learning (ML) has seen a significant surge and uptake across many diverse applications. The high flexibility, adaptability and computing capabilities it provides extends tr…
Rethinking the Trigger of Backdoor Attack
Yiming Li, Tongqing Zhai, Baoyuan Wu +3
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of the infected model will be maliciously changed if the hidden bac…
Deep Flow Collaborative Network for Online Visual Tracking
Peidong Liu, Xiyu Yan, Yong Jiang +1
The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network. However, the tracki…
Adversarial Defense via Local Flatness Regularization
Jia Xu, Yiming Li, Yong Jiang +1
Adversarial defense is a popular and important research area. Due to its intrinsic mechanism, one of the most straightforward and effective ways of defending attacks is to analyze…
DAL: Dual Adversarial Learning for Dialogue Generation
Shaobo Cui, Rongzhong Lian, Di Jiang +3
In open-domain dialogue systems, generative approaches have attracted much attention for response generation. However, existing methods are heavily plagued by generating safe respo…