% Key papers for Training-time backdoor defense (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2021_neural_30070,
  title = {{Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2760},
}

@inproceedings{cvpr2023_backdoor_20444,
  title = {{Backdoor Defense via Adaptively Splitting Poisoned Dataset}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22486},
}

@inproceedings{cvpr2023_backdoor_20445,
  title = {{Backdoor Defense via Deconfounded Representation Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/22160},
}

@inproceedings{cvpr2022_few_18847,
  title = {{Few-Shot Backdoor Defense Using Shapley Estimation}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2021_anti_62697,
  title = {{Anti-Backdoor Learning: Training Clean Models on Poisoned Data}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27835},
}

@inproceedings{cvpr2023_detecting_20775,
  title = {{Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/20970},
}

@inproceedings{neurips2021_adversarial_61321,
  title = {{Adversarial Neuron Pruning Purifies Backdoored Deep Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/27055},
}

@inproceedings{cvpr2023_backdoor_20443,
  title = {{Backdoor Cleansing With Unlabeled Data}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21829},
}

@inproceedings{cvpr2023_progressive_21923,
  title = {{Progressive Backdoor Erasing via Connecting Backdoor and Adversarial Attacks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21203},
}

@inproceedings{iclr2023_scale_33164,
  title = {{SCALE-UP: An Efficient Black-box Input-level Backdoor Detection via Analyzing Scaled Prediction Consistency}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11731},
}

@inproceedings{iclr2022_aeva_31216,
  title = {{AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6929},
}

@inproceedings{aaai2024_progressive_7003,
  title = {{Progressive Poisoned Data Isolation for Training-Time Backdoor Defense}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29023},
}

@inproceedings{aaai2024_inspecting_6325,
  title = {{Inspecting Prediction Confidence for Detecting Black-Box Backdoor Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27780},
}

@inproceedings{iclr2022_how_30992,
  title = {{How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6256},
}

@inproceedings{aaai2025_adversarial_8063,
  title = {{Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33030},
}

@inproceedings{cvpr2024_backdoor_22815,
  title = {{Backdoor Defense via Test-Time Detecting and Repairing}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/29483},
}

@inproceedings{neurips2023_neural_68338,
  title = {{Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71467},
}

@inproceedings{neurips2023_towards_67072,
  title = {{Towards Stable Backdoor Purification through Feature Shift Tuning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72630},
}

@inproceedings{neurips2022_pre_66390,
  title = {{Pre-activation Distributions Expose Backdoor Neurons}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/54164},
}

@inproceedings{neurips2024_ban_72880,
  title = {{BAN: Detecting Backdoors Activated by Adversarial Neuron Noise}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94526},
}

@inproceedings{iclr2024_badexpert_35407,
  title = {{BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17666},
}

@inproceedings{icml2024_ibd_44673,
  title = {{IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33779},
}

@inproceedings{neurips2024_breaking_71167,
  title = {{Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96060},
}

@inproceedings{aaai2025_fusing_9258,
  title = {{Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34828},
}

@inproceedings{neurips2024_unveiling_71796,
  title = {{Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95499},
}

@inproceedings{neurips2024_uncovering_74034,
  title = {{Uncovering, Explaining, and Mitigating the Superficial Safety of Backdoor Defense}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93490},
}

@inproceedings{neurips2025_backdoor_80034,
  title = {{Backdoor Mitigation via Invertible Pruning Masks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=vOAtjgCAAO},
}

@inproceedings{icml2025_circumventing_47991,
  title = {{Circumventing Backdoor Space via Weight Symmetry}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44614},
}

@inproceedings{icml2026_statistically_50480,
  title = {{Statistically Undetectable Backdoors in Deep Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=Clh5CRZpjD},
}

@inproceedings{neurips2025_losplit_74996,
  title = {{LoSplit: Loss-Guided Dynamic Split for Training-Time Defense Against Graph Backdoor Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=3Cpw7YftBm},
}

@inproceedings{iclr2025_refine_80748,
  title = {{REFINE: Inversion-Free Backdoor Defense via Model Reprogramming}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31020},
}

@inproceedings{aaai2026_bedkd_108550,
  title = {{BeDKD: Backdoor Defense Based on Directional Mapping Module and Adversarial Knowledge Distillation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40902},
}

@inproceedings{icml2024_energy_44474,
  title = {{Energy-based Backdoor Defense without Task-Specific Samples and Model Retraining}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33978},
}

@inproceedings{icml2025_bi_46877,
  title = {{Bi-perspective Splitting Defense: Achieving Clean-Seed-Free Backdoor Security}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45701},
}

@inproceedings{cvpr2026_logit_99047,
  title = {{Logit-Margin Repulsion for Backdoor Defense}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Yang_Logit-Margin_Repulsion_for_Backdoor_Defense_CVPR_2026_paper.html},
}