26 citations · 42 across the 4 of their papers we have counts for
6 papers · 1 filter
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Zhanke Zhou, Chenyu Zhou, Xuan Li +3
Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored. To close this gap, we perform the f…
Moderately Distributional Exploration for Domain Generalization
Rui Dai, Yonggang Zhang, Zhen Fang +2
Domain generalization (DG) aims to tackle the distribution shift between training domains and unknown target domains. Generating new domains is one of the most effective approaches…
Understanding and Improving Feature Learning for Out-of-Distribution Generalization
Yongqiang Chen, Wei Huang, Kaiwen Zhou +3
A common explanation for the failure of out-of-distribution (OOD) generalization is that the model trained with empirical risk minimization (ERM) learns spurious features instead o…
Watermarking for Out-of-distribution Detection
Qizhou Wang, Feng Liu, Yonggang Zhang +4
Out-of-distribution (OOD) detection aims to identify OOD data based on representations extracted from well-trained deep models. However, existing methods largely ignore the reprogr…
Towards Lightweight Black-Box Attacks against Deep Neural Networks
Chenghao Sun, Yonggang Zhang, Wan Chaoqun +5
Black-box attacks can generate adversarial examples without accessing the parameters of target model, largely exacerbating the threats of deployed deep neural networks (DNNs). Howe…
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Yongqiang Chen, Han Yang, Yonggang Zhang +4
Recently Graph Injection Attack (GIA) emerges as a practical attack scenario on Graph Neural Networks (GNNs), where the adversary can merely inject few malicious nodes instead of m…