% Key papers for Decision-boundary geometry of robustness (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{cvpr2016_deepfool_11484,
  title = {{DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2016},
}

@inproceedings{cvpr2018_lightweight_13336,
  title = {{Lightweight Probabilistic Deep Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Gast_Lightweight_Probabilistic_Deep_CVPR_2018_paper.html},
}

@inproceedings{cvpr2018_empirical_13067,
  title = {{Empirical Study of the Topology and Geometry of Deep Networks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Fawzi_Empirical_Study_of_CVPR_2018_paper.html},
}

@inproceedings{cvpr2018_geometric_13156,
  title = {{Geometric Robustness of Deep Networks: Analysis and Improvement}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Kanbak_Geometric_Robustness_of_CVPR_2018_paper.html},
}

@inproceedings{iclr2019_hierarchical_29057,
  title = {{Hierarchical interpretations for neural network predictions}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/681},
}

@inproceedings{iclr2020_mma_29496,
  title = {{MMA Training: Direct Input Space Margin Maximization through Adversarial Training}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=HkeryxBtPB},
}

@inproceedings{iclr2018_decision_28454,
  title = {{Decision Boundary Analysis of Adversarial Examples}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/57},
}

@inproceedings{cvpr2019_on_14606,
  title = {{On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2021_maxup_17392,
  title = {{MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2021_regularizing_17729,
  title = {{Regularizing Neural Networks via Adversarial Model Perturbation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2021},
}

@inproceedings{cvpr2019_a_13776,
  title = {{A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2020_sam_16150,
  title = {{SAM: The Sensitivity of Attribution Methods to Hyperparameters}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2018_robustness_28474,
  title = {{Robustness of Classifiers to Universal Perturbations: A Geometric Perspective}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/286},
}

@inproceedings{icml2018_max_36843,
  title = {{Max-Mahalanobis Linear Discriminant Analysis Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2079},
}

@inproceedings{icml2019_on_37602,
  title = {{On the Connection Between Adversarial Robustness and Saliency Map Interpretability}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2019},
  url = {https://icml.cc/virtual/2019/poster/4176},
}

@inproceedings{icml2020_concise_38032,
  title = {{Concise Explanations of Neural Networks using Adversarial Training}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2020},
  url = {https://icml.cc/virtual/2020/poster/6609},
}

@inproceedings{neurips2020_smoothed_60843,
  title = {{Smoothed Geometry for Robust Attribution}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18143},
}

@inproceedings{cvpr2018_analytic_12837,
  title = {{Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Bibi_Analytic_Expressions_for_CVPR_2018_paper.html},
}

@inproceedings{iclr2019_adef_28924,
  title = {{ADef: an Iterative Algorithm to Construct Adversarial Deformations}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/706},
}

@inproceedings{iclr2020_adversarial_29333,
  title = {{Adversarial Lipschitz Regularization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=Bke_DertPB},
}

@inproceedings{neurips2024_curvature_72739,
  title = {{Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94651},
}

@inproceedings{neurips2024_training_71428,
  title = {{Training for Stable Explanation for Free}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95826},
}

@inproceedings{cvpr2024_structured_24903,
  title = {{Structured Gradient-based Interpretations via Norm-Regularized Adversarial Training}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31744},
}

@inproceedings{aaai2025_contrasting_8528,
  title = {{Contrasting Adversarial Perturbations: The Space of Harmless Perturbations}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32209},
}

@inproceedings{aaai2025_verification_11229,
  title = {{Verification of Neural Networks Against Convolutional Perturbations via Parameterised Kernels}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34930},
}

@inproceedings{icml2024_improving_44686,
  title = {{Improving Interpretation Faithfulness for Vision Transformers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33766},
}

@inproceedings{icml2024_deep_45820,
  title = {{Deep Networks Always Grok and Here is Why}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32632},
}

@inproceedings{icml2025_adversarial_47289,
  title = {{Adversarial Inputs for Linear Algebra Backends}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45300},
}

@inproceedings{iclr2026_counterfactual_85156,
  title = {{Counterfactual Explanations on Robust Perceptual Geodesics}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=B5RBKM4vPY},
}

@inproceedings{iclr2026_perturbation_85585,
  title = {{Perturbation-Induced Linearization: Constructing Unlearnable Data with Solely Linear Classifiers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=GBSGToE97J},
}

@inproceedings{iclr2026_fingerprinting_88933,
  title = {{Fingerprinting Deep Neural Networks for Ownership Protection: An Analytical Approach}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=sg3UNWKVFt},
}

@inproceedings{neurips2026_interpretable_93269,
  title = {{Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/154045},
}

@inproceedings{iclr2026_fame_86911,
  title = {{FAME: \$underlineF\$ormal \$underlineA\$bstract \$underlineM\$inimal \$underlineE\$xplanation for neural networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=VJkNqJJAhV},
}