% Key papers for Adversarial training with feature consistency (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{neurips2018_generating_57168,
  title = {{Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11194},
}

@inproceedings{icml2020_adversarial_37916,
  title = {{Adversarial Filters of Dataset Biases}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6628},
}

@inproceedings{aaai2020_adversarially_101955,
  title = {{Adversarially Robust Distillation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5816},
}

@inproceedings{cvpr2021_regularizing_17728,
  title = {{Regularizing Generative Adversarial Networks Under Limited Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2019_fooling_58294,
  title = {{Fooling Neural Network Interpretations via Adversarial Model Manipulation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13476},
}

@inproceedings{neurips2018_invariant_57250,
  title = {{Invariant Representations without Adversarial Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11866},
}

@inproceedings{cvpr2018_interpret_13232,
  title = {{Interpret Neural Networks by Identifying Critical Data Routing Paths}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Interpret_Neural_Networks_CVPR_2018_paper.html},
}

@inproceedings{aaai2022_improved_2230,
  title = {{Improved Text Classification via Contrastive Adversarial Training}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/11130-improved-text-classification-via-contrastive-adversarial-training/},
}

@inproceedings{neurips2021_are_62899,
  title = {{Are Transformers more robust than CNNs?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28481},
}

@inproceedings{cvpr2020_transferable_16380,
  title = {{Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{neurips2019_accurate_57864,
  title = {{Accurate, reliable and fast robustness evaluation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14233},
}

@inproceedings{neurips2020_contrastive_59595,
  title = {{Contrastive Learning with Adversarial Examples}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17291},
}

@inproceedings{cvpr2020_gamin_15578,
  title = {{GAMIN: Generative Adversarial Multiple Imputation Network for Highly Missing Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2021_libre_17344,
  title = {{LiBRe: A Practical Bayesian Approach to Adversarial Detection}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{icml2021_measuring_39598,
  title = {{Measuring Robustness in Deep Learning Based Compressive Sensing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9951},
}

@inproceedings{neurips2020_adversarial_59343,
  title = {{Adversarial Learning for Robust Deep Clustering}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17742},
}

@inproceedings{neurips2018_learning_57325,
  title = {{Learning to Repair Software Vulnerabilities with Generative Adversarial Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11761},
}

@inproceedings{cvpr2021_lafeat_17234,
  title = {{LAFEAT: Piercing Through Adversarial Defenses With Latent Features}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{neurips2020_robust_60729,
  title = {{Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18912},
}

@inproceedings{cvpr2020_towards_16351,
  title = {{Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2024_pre_24504,
  title = {{Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29705},
}

@inproceedings{cvpr2024_defense_23134,
  title = {{Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30682},
}

@inproceedings{aaai2024_sparse_7377,
  title = {{Sparse Enhanced Network: An Adversarial Generation Method for Robust Augmentation in Sequential Recommendation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28669},
}

@inproceedings{aaai2024_improving_6307,
  title = {{Improving the Adversarial Transferability of Vision Transformers with Virtual Dense Connection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28541},
}

@inproceedings{cvpr2024_robust_24667,
  title = {{Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31739},
}

@inproceedings{cvpr2024_adversarial_22735,
  title = {{Adversarial Distillation Based on Slack Matching and Attribution Region Alignment}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30246},
}

@inproceedings{aaai2024_out_6881,
  title = {{Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28390},
}

@inproceedings{cvpr2025_clip_25653,
  title = {{CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/35254},
}

@inproceedings{aaai2024_harnessing_6190,
  title = {{Harnessing Edge Information for Improved Robustness in Vision Transformers}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28110},
}

@inproceedings{cvpr2024_random_24575,
  title = {{Random Entangled Tokens for Adversarially Robust Vision Transformer}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29256},
}

@inproceedings{cvpr2025_taet_27801,
  title = {{TAET: Two-Stage Adversarial Equalization Training on Long-Tailed  Distributions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/35250},
}

@inproceedings{aaai2024_representation_7112,
  title = {{Representation-Based Robustness in Goal-Conditioned Reinforcement Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30176},
}

@inproceedings{aaai2024_robust_7172,
  title = {{Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29904},
}

@inproceedings{iclr2025_speech_81288,
  title = {{Speech Robust Bench: A Robustness Benchmark For Speech Recognition}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30483},
}

@inproceedings{aaai2025_rp_10452,
  title = {{RP-PGD: Boosting Segmentation Robustness with a Region-and-Prototype Based Adversarial Attack}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33122},
}

@inproceedings{cvpr2024_adversarially_22738,
  title = {{Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31617},
}

@inproceedings{aaai2025_efficient_8919,
  title = {{Efficient Robustness Evaluation via Constraint Relaxation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32670},
}

@inproceedings{aaai2025_mitigating_9959,
  title = {{Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33176},
}

@inproceedings{neurips2024_great_74381,
  title = {{GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93184},
}

@inproceedings{iclr2025_i_80614,
  title = {{I Can Hear You: Selective Robust Training for Deepfake Audio Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/32111},
}