Can Adversarial Examples be Parsed to Reveal Victim Model Information?
- Yuguang Yao
- Jiancheng Liu
- et al.
- 2025
- WACV 2025
Conference paper
Publications
17 results for Xue Lin
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations
- Pu Zhao
- Parikshit Ram
- et al.
- 2022
- IJCAI 2022
Conference paperStructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs
- Tianyun Zhang
- Shaokai Ye
- et al.
- 2022
- IEEE TNNLS
PaperReverse Engineering of Imperceptible Adversarial Image Perturbations
- Yuguang Yao
- Yifan Gong
- et al.
- 2022
- ICLR 2022
Conference paperMEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
- Geng Yuan
- Xialong Ma
- et al.
- 2021
- NeurIPS 2021
Conference paperAdvMS: A Multi-Source Multi-Cost Defense Against Adversarial Attacks
- Xiao Wang
- Siyue Wang
- et al.
- 2020
- ICASSP 2020
Conference paperTowards an efficient and general framework of robust training for graph neural networks
- Kaidi Xu
- Sijia Liu
- et al.
- 2020
- ICASSP 2020
Conference paperTowards query-efficient black-box adversary with zeroth-order natural gradient descent
- Pu Zhao
- Pin-Yu Chen
- et al.
- 2020
- AAAI 2020
Conference paperTowards certificated model robustness against weight perturbations
- Tsui Wei Weng
- Pu Zhao
- et al.
- 2020
- AAAI 2020
Conference paperZO-AdaMM: Zeroth-order adaptive momentum method for black-box optimization
- Xiangyi Chen
- Sijia Liu
- et al.
- 2019
- NeurIPS 2019
Conference paper