no code implementations • 16 Sep 2023 • Yaguan Qian, Boyuan Ji, Shuke He, Shenhui Huang, Xiang Ling, Bin Wang, Wei Wang
However, these models are vulnerable to backdoor attacks.
no code implementations • 16 Jul 2022 • Xiaoyu Liang, Yaguan Qian, Jianchang Huang, Xiang Ling, Bin Wang, Chunming Wu, Wassim Swaileh
Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models.
1 code implementation • ECCV 2022 • Yaguan Qian, Shenghui Huang, Bin Wang, Xiang Ling, Xiaohui Guan, Zhaoquan Gu, Shaoning Zeng, WuJie Zhou, Haijiang Wang
This process is modeled as a multi-objective bilevel optimization problem and a novel algorithm is proposed to solve this optimization.
1 code implementation • 23 Dec 2021 • Xiang Ling, Lingfei Wu, Jiangyu Zhang, Zhenqing Qu, Wei Deng, Xiang Chen, Yaguan Qian, Chunming Wu, Shouling Ji, Tianyue Luo, Jingzheng Wu, Yanjun Wu
Then, we conduct a comprehensive and systematic review to categorize the state-of-the-art adversarial attacks against PE malware detection, as well as corresponding defenses to increase the robustness of Windows PE malware detection.
no code implementations • 1 Jan 2021 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs.
no code implementations • 1 Jan 2021 • Yaguan Qian, Jiamin Wang, Xiang Ling, Zhaoquan Gu, Bin Wang, Chunming Wu
Recently, to deal with the vulnerability to generate examples of CNNs, there are many advanced algorithms that have been proposed.
no code implementations • 24 Oct 2020 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
To this end, we first represent both natural language query texts and programming language code snippets with the unified graph-structured data, and then use the proposed graph matching and searching model to retrieve the best matching code snippet.
1 code implementation • 8 Jul 2020 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, Shouling Ji
In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs.
no code implementations • 25 Sep 2019 • Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Chunming Wu, Shouling Ji
The proposed HGMN model consists of a multi-perspective node-graph matching network for effectively learning cross-level interactions between parts of a graph and a whole graph, and a siamese graph neural network for learning global-level interactions between two graphs.