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2020 – today
- 2024
[c9]Wei-Jia Chen, Chia-Yi Hsu, Wei-Bin Lee, Chia-Mu Yu, Chun-Ying Huang:
Road Decals as Trojans: Disrupting Autonomous Vehicle Navigation with Adversarial Patterns. DSN-S 2024: 133-140- [c8]Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, Chun-Ying Huang:
Ring-A-Bell! How Reliable are Concept Removal Methods For Diffusion Models? ICLR 2024 - [i9]Chia-Yi Hsu, Yu-Lin Tsai, Chih-Hsun Lin, Pin-Yu Chen, Chia-Mu Yu, Chun-Ying Huang:
Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models. CoRR abs/2405.16833 (2024) - 2023
- [j1]Yen-Ting Chen, Chia-Yi Hsu, Chia-Mu Yu
, Mahmoud Barhamgi, Charith Perera:
On the Private Data Synthesis Through Deep Generative Models for Data Scarcity of Industrial Internet of Things. IEEE Trans. Ind. Informatics 19(1): 551-560 (2023) - [i8]Chih-Hsun Lin, Chia-Yi Hsu, Chia-Mu Yu, Yang Cao, Chun-Ying Huang:
DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase. CoRR abs/2304.12185 (2023) - [i7]Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, Chun-Ying Huang:
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models? CoRR abs/2310.10012 (2023) - 2022
- [c7]Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Chia-Mu Yu:
Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning. AAAI 2022: 6926-6934 - [c6]Chang-Sheng Lin, Chia-Yi Hsu, Pin-Yu Chen, Chia-Mu Yu:
Real-World Adversarial Examples Via Makeup. ICASSP 2022: 2854-2858 - 2021
- [c5]Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen:
Non-Singular Adversarial Robustness of Neural Networks. ICASSP 2021: 3840-3844 - [c4]Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen:
Catastrophic Data Leakage in Vertical Federated Learning. NeurIPS 2021: 994-1006 - [c3]Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen:
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations. NeurIPS 2021: 19692-19704 - [i6]Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen:
Non-Singular Adversarial Robustness of Neural Networks. CoRR abs/2102.11935 (2021) - [i5]Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Lu, Chia-Mu Yu:
Adversarial Examples for Unsupervised Machine Learning Models. CoRR abs/2103.01895 (2021) - [i4]Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen:
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations. CoRR abs/2103.02200 (2021) - [i3]Chang-Sheng Lin, Chia-Yi Hsu, Pin-Yu Chen, Chia-Mu Yu:
Real-World Adversarial Examples involving Makeup Application. CoRR abs/2109.03329 (2021) - [i2]Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen:
CAFE: Catastrophic Data Leakage in Vertical Federated Learning. CoRR abs/2110.15122 (2021)
2010 – 2019
- 2019
- [c2]Chia-Yi Hsu, Pin-Yu Chen, Chia-Mu Yu:
Characterizing Adversarial Subspaces by Mutual Information. AsiaCCS 2019: 667-669 - 2018
- [c1]Chia-Yi Hsu, Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu:
On the Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces. GlobalSIP 2018: 1149-1153 - [i1]Chia-Yi Hsu, Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu:
On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces. CoRR abs/1809.08986 (2018)
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