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2020 – today
- 2025
[j4]Xiangyuan Yang
, Jie Lin, Hanlin Zhang, Peng Zhao:
Rethinking the optimization objective for transferable adversarial examples from a fuzzy perspective. Neural Networks 184: 107019 (2025)- 2024
- [j3]Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao:
An auxiliary correction model implemented by the Correction Property of intermediate layer against adversarial examples. Appl. Soft Comput. 167: 112367 (2024) - [j2]Xiangyuan Yang, Jie Lin, Hanlin Zhang, Peng Zhao:
Improving query efficiency of black-box attacks via the preference of deep learning models. Inf. Sci. 678: 121013 (2024) - [i8]Xiaowei Long, Jie Lin, Xiangyuan Yang:
Adversarial Detection with a Dynamically Stable System. CoRR abs/2411.06666 (2024) - [i7]Hao Li, Xiangyuan Yang, Mengzhu Wang, Long Lan, Ke Liang, Xinwang Liu, Kenli Li:
Object Style Diffusion for Generalized Object Detection in Urban Scene. CoRR abs/2412.13815 (2024) - 2023
- [j1]Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao:
Improving the transferability of adversarial examples via direction tuning. Inf. Sci. 647: 119491 (2023) - [c1]Zhaoxi Mu, Xinyu Yang, Xiangyuan Yang, Wenjing Zhu:
A Multi-Stage Triple-Path Method For Speech Separation in Noisy and Reverberant Environments. ICASSP 2023: 1-5 - [i6]Zhaoxi Mu, Xinyu Yang, Xiangyuan Yang, Wenjing Zhu:
A Multi-Stage Triple-Path Method for Speech Separation in Noisy and Reverberant Environments. CoRR abs/2303.03732 (2023) - [i5]Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao:
Fuzziness-tuned: Improving the Transferability of Adversarial Examples. CoRR abs/2303.10078 (2023) - [i4]Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao:
Improving the Transferability of Adversarial Examples via Direction Tuning. CoRR abs/2303.15109 (2023) - 2022
- [i3]Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao:
Enhancing the Transferability of Adversarial Examples via a Few Queries. CoRR abs/2205.09518 (2022) - [i2]Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao:
Mask-Guided Divergence Loss Improves the Generalization and Robustness of Deep Neural Network. CoRR abs/2206.00913 (2022) - [i1]Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang, Peng Zhao:
FACM: Correct the Output of Deep Neural Network with Middle Layers Features against Adversarial Samples. CoRR abs/2206.00924 (2022)
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