Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
- Gaoyuan Zhang
- Songtao Lu
- et al.
- 2022
- UAI 2022
Publications
This is our catalog of publications authored by IBM researchers, in collaboration with the global research community. It’s an ever-growing body of work that shows why IBM is one of the most important contributors to modern computing.
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Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations
- Pu Zhao
- Parikshit Ram
- et al.
- 2022
- IJCAI 2022
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free
- Tianlong Chen
- Zhenyu Zhang
- et al.
- 2022
- CVPR 2022
Third Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2021)
- Pin-Yu Chen
- Cho-Jui Hsieh
- et al.
- 2021
- KDD 2021
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
- Tianlong Chen
- Sijia Liu
- et al.
- 2020
- CVPR 2020
Towards verifying robustness of neural networks against a family of semantic perturbations
- Jeet Mohapatra
- Tsui Wei Weng
- et al.
- 2020
- CVPR 2020
Towards an efficient and general framework of robust training for graph neural networks
- Kaidi Xu
- Sijia Liu
- et al.
- 2020
- ICASSP 2020
Guaranteed Convergence of Training Convolutional Neural Networks via Accelerated Gradient Descent
- Shuai Zhang
- Meng Wang
- et al.
- 2020
- CISS 2020
Generation of low distortion adversarial attacks via convex programming
- Tianyun Zhang
- Sijia Liu
- et al.
- 2019
- ICDM 2019
On the design of black-box adversarial examples by leveraging gradient-free optimization and operator splitting method
- Pu Zhao
- Sijia Liu
- et al.
- 2019
- ICCV 2019