Saved in:
| Main Authors: | Xie, Xinheng, Wu, Yue, He, Cuiyu |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.03539 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN
by: Zhang, Jiayu, et al.
Published: (2025)
by: Zhang, Jiayu, et al.
Published: (2025)
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
by: Zhu, Zhiyu, et al.
Published: (2024)
by: Zhu, Zhiyu, et al.
Published: (2024)
AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses
by: Carlini, Nicholas, et al.
Published: (2025)
by: Carlini, Nicholas, et al.
Published: (2025)
Seismic full-waveform inversion based on a physics-driven generative adversarial network
by: Zhang, Xinyi, et al.
Published: (2026)
by: Zhang, Xinyi, et al.
Published: (2026)
Deep generative models as an adversarial attack strategy for tabular machine learning
by: Dyrmishi, Salijona, et al.
Published: (2024)
by: Dyrmishi, Salijona, et al.
Published: (2024)
Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity
by: Zhang, Yifu, et al.
Published: (2023)
by: Zhang, Yifu, et al.
Published: (2023)
Enhancing robustness of data-driven SHM models: adversarial training with circle loss
by: Yang, Xiangli, et al.
Published: (2024)
by: Yang, Xiangli, et al.
Published: (2024)
Properties that allow or prohibit transferability of adversarial attacks among quantized networks
by: Shrestha, Abhishek, et al.
Published: (2024)
by: Shrestha, Abhishek, et al.
Published: (2024)
Can Go AIs be adversarially robust?
by: Tseng, Tom, et al.
Published: (2024)
by: Tseng, Tom, et al.
Published: (2024)
Well log data generation and imputation using sequence-based generative adversarial networks
by: Al-Fakih, Abdulrahman, et al.
Published: (2024)
by: Al-Fakih, Abdulrahman, et al.
Published: (2024)
Robust NAS under adversarial training: benchmark, theory, and beyond
by: Wu, Yongtao, et al.
Published: (2024)
by: Wu, Yongtao, et al.
Published: (2024)
ADBM: Adversarial diffusion bridge model for reliable adversarial purification
by: Li, Xiao, et al.
Published: (2024)
by: Li, Xiao, et al.
Published: (2024)
Deep MMD Gradient Flow without adversarial training
by: Galashov, Alexandre, et al.
Published: (2024)
by: Galashov, Alexandre, et al.
Published: (2024)
Simulating realistic short tandem repeat capillary electrophoretic signal using a generative adversarial network
by: Taylor, Duncan, et al.
Published: (2024)
by: Taylor, Duncan, et al.
Published: (2024)
An attempt to generate new bridge types from latent space of generative adversarial network
by: Zhang, Hongjun
Published: (2024)
by: Zhang, Hongjun
Published: (2024)
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation
by: Sun, Peiran
Published: (2025)
by: Sun, Peiran
Published: (2025)
Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning
by: Sclar, Melanie, et al.
Published: (2024)
by: Sclar, Melanie, et al.
Published: (2024)
Missing value imputation with adversarial random forests -- MissARF
by: Golchian, Pegah, et al.
Published: (2025)
by: Golchian, Pegah, et al.
Published: (2025)
Fixed-point graph convolutional networks against adversarial attacks
by: Khan, Shakib, et al.
Published: (2025)
by: Khan, Shakib, et al.
Published: (2025)
Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness
by: Ballarin, Emanuele, et al.
Published: (2023)
by: Ballarin, Emanuele, et al.
Published: (2023)
Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection
by: Wu, Yinting, et al.
Published: (2024)
by: Wu, Yinting, et al.
Published: (2024)
Are aligned neural networks adversarially aligned?
by: Carlini, Nicholas, et al.
Published: (2023)
by: Carlini, Nicholas, et al.
Published: (2023)
RDI: An adversarial robustness evaluation metric for deep neural networks based on model statistical features
by: Song, Jialei, et al.
Published: (2025)
by: Song, Jialei, et al.
Published: (2025)
Open-weight genome language model safeguards: Assessing robustness via adversarial fine-tuning
by: Black, James R. M., et al.
Published: (2025)
by: Black, James R. M., et al.
Published: (2025)
How adversarial attacks can disrupt seemingly stable accurate classifiers
by: Sutton, Oliver J., et al.
Published: (2023)
by: Sutton, Oliver J., et al.
Published: (2023)
Perturbation: A simple and efficient adversarial tracer for representation learning in language models
by: Rozner, Joshua, et al.
Published: (2026)
by: Rozner, Joshua, et al.
Published: (2026)
Synthetic data generation for system identification: leveraging knowledge transfer from similar systems
by: Piga, Dario, et al.
Published: (2024)
by: Piga, Dario, et al.
Published: (2024)
Empirical evaluation of the Frank-Wolfe methods for constructing white-box adversarial attacks
by: Korotkova, Kristina, et al.
Published: (2025)
by: Korotkova, Kristina, et al.
Published: (2025)
Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning
by: Mohammadi, Hadi, et al.
Published: (2025)
by: Mohammadi, Hadi, et al.
Published: (2025)
Quantum entanglement provides a competitive advantage in adversarial games
by: Wang, Peiyong, et al.
Published: (2026)
by: Wang, Peiyong, et al.
Published: (2026)
Guiding the retraining of convolutional neural networks against adversarial inputs
by: López, Francisco Durán, et al.
Published: (2022)
by: López, Francisco Durán, et al.
Published: (2022)
Evaluating the robustness of adversarial defenses in malware detection systems
by: Jafari, Mostafa, et al.
Published: (2025)
by: Jafari, Mostafa, et al.
Published: (2025)
Benford's law: what does it say on adversarial images?
by: Zago, João G., et al.
Published: (2021)
by: Zago, João G., et al.
Published: (2021)
From text to multimodal: a survey of adversarial example generation in question answering systems
by: Yigit, Gulsum, et al.
Published: (2023)
by: Yigit, Gulsum, et al.
Published: (2023)
Detection of adversarial intent in Human-AI teams using LLMs
by: Musaffar, Abed K., et al.
Published: (2026)
by: Musaffar, Abed K., et al.
Published: (2026)
Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise
by: Chicchi, Lorenzo, et al.
Published: (2024)
by: Chicchi, Lorenzo, et al.
Published: (2024)
PolyNODE: Variable-dimension Neural ODEs on M-polyfolds
by: Åhag, Per, et al.
Published: (2026)
by: Åhag, Per, et al.
Published: (2026)
Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning
by: García-Márquez, Mario, et al.
Published: (2025)
by: García-Márquez, Mario, et al.
Published: (2025)
Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG
by: Guo, Manshan, et al.
Published: (2024)
by: Guo, Manshan, et al.
Published: (2024)
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning
by: Mumuni, Fuseinin, et al.
Published: (2024)
by: Mumuni, Fuseinin, et al.
Published: (2024)
Similar Items
-
PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN
by: Zhang, Jiayu, et al.
Published: (2025) -
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
by: Zhu, Zhiyu, et al.
Published: (2024) -
AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses
by: Carlini, Nicholas, et al.
Published: (2025) -
Seismic full-waveform inversion based on a physics-driven generative adversarial network
by: Zhang, Xinyi, et al.
Published: (2026) -
Deep generative models as an adversarial attack strategy for tabular machine learning
by: Dyrmishi, Salijona, et al.
Published: (2024)