76 citations · 94 across the 7 of their papers we have counts for
10 papers
A Unified Game-Theoretic Interpretation of Adversarial Robustness
Jie Ren, Die Zhang, Yisen Wang +8
This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i.e.} the view of multi-order interactions between input variables of DNNs. B…
Backdoor Pre-trained Models Can Transfer to All
Lujia Shen, Shouling Ji, Xuhong Zhang +6
Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model wit…
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack
Xinyi Zhang, Chengfang Fang, Jie Shi
Model extraction increasingly attracts research attentions as keeping commercial AI models private can retain a competitive advantage. In some scenarios, AI models are trained prop…
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
An Zhang, Xiang Wang, Chengfang Fang +3
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs). However, the redundancy of attribution features and the gradient saturation…
DAFAR: Defending against Adversaries by Feedback-Autoencoder Reconstruction
Haowen Liu, Ping Yi, Hsiao-Ying Lin +2
Deep learning has shown impressive performance on challenging perceptual tasks and has been widely used in software to provide intelligent services. However, researchers found deep…
A Unified Game-Theoretic Interpretation of Adversarial Robustness
Jie Ren, Die Zhang, Yisen Wang +8
This paper provides a unified view to explain different adversarial attacks and defense methods, i.e. the view of multi-order interactions between input variables of DNNs. Based on…