% Key papers for Regularized defenses against attacks (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2020_adversarial_15128,
  title = {{Adversarial Examples Improve Image Recognition}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2018_pixeldefend_28673,
  title = {{PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/163},
}

@inproceedings{cvpr2019_decoupling_14013,
  title = {{Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2019_adversarial_57880,
  title = {{Adversarial Robustness through Local Linearization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14313},
}

@inproceedings{aaai2020_universal_102155,
  title = {{Universal Adversarial Training}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6017},
}

@inproceedings{iclr2019_structured_28805,
  title = {{Structured Adversarial Attack: Towards General Implementation and Better Interpretability}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/859},
}

@inproceedings{neurips2020_boosting_59478,
  title = {{Boosting Adversarial Training with Hypersphere Embedding}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17792},
}

@inproceedings{neurips2018_deep_56999,
  title = {{Deep Defense: Training DNNs with Improved Adversarial Robustness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11066},
}

@inproceedings{neurips2019_functional_58305,
  title = {{Functional Adversarial Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14054},
}

@inproceedings{iclr2019_improving_29089,
  title = {{Improving the Generalization of Adversarial Training with Domain Adaptation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/675},
}

@inproceedings{cvpr2020_learn2perturb_15740,
  title = {{Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{cvpr2019_trust_14949,
  title = {{Trust Region Based Adversarial Attack on Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{iclr2020_mixup_29395,
  title = {{Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=ByxtC2VtPB},
}

@inproceedings{iclr2019_defensive_29211,
  title = {{Defensive Quantization: When Efficiency Meets Robustness}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/863},
}

@inproceedings{icml2020_proper_38650,
  title = {{Proper Network Interpretability Helps Adversarial Robustness in Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6031},
}

@inproceedings{iclr2019_adversarial_29105,
  title = {{Adversarial Reprogramming of Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/1124},
}

@inproceedings{iclr2021_perceptual_30478,
  title = {{Perceptual Adversarial Robustness: Defense Against Unseen Threat Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2860},
}

@inproceedings{aaai2025_attackbench_8167,
  title = {{AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32263},
}

@inproceedings{aaai2024_taxonomy_7507,
  title = {{Taxonomy Driven Fast Adversarial Training}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28330},
}

@inproceedings{neurips2019_learning_58526,
  title = {{Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14169},
}

@inproceedings{iclr2024_adversarial_35486,
  title = {{Adversarial Training on Purification (AToP): Advancing Both Robustness and Generalization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17587},
}

@inproceedings{icml2024_two_45680,
  title = {{Two Heads are Actually Better than One: Towards Better Adversarial Robustness via Transduction and Rejection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32772},
}

@inproceedings{icml2025_one_48630,
  title = {{One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43991},
}

@inproceedings{neurips2024_malt_72905,
  title = {{MALT Powers Up Adversarial Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94506},
}

@inproceedings{iclr2025_gse_82789,
  title = {{GSE: Group-wise Sparse and Explainable Adversarial Attacks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29010},
}

@inproceedings{iclr2024_on_35289,
  title = {{On Adversarial Training without Perturbing all Examples}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17784},
}

@inproceedings{neurips2025_vulnerable_80371,
  title = {{Vulnerable Data-Aware Adversarial Training}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=yrrU5YChQr},
}