% Key papers for Detecting poisoned samples (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2022_adversarial_31186,
  title = {{Adversarial Unlearning of Backdoors via Implicit Hypergradient}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6180},
}

@inproceedings{cvpr2023_defending_20756,
  title = {{Defending Against Patch-Based Backdoor Attacks on Self-Supervised Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21779},
}

@inproceedings{neurips2022_a_65784,
  title = {{A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55741},
}

@inproceedings{cvpr2024_nearest_24267,
  title = {{Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/30600},
}

@inproceedings{aaai2024_dataelixir_5602,
  title = {{DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30186},
}

@inproceedings{icml2021_spectre_39910,
  title = {{SPECTRE: defending against backdoor attacks using robust statistics}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://proceedings.mlr.press/v139/hayase21a.html},
}

@inproceedings{neurips2023_shared_70103,
  title = {{Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/69874},
}

@inproceedings{icml2022_constrained_40358,
  title = {{Constrained Optimization with Dynamic Bound-scaling for Effective NLP Backdoor Defense}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17165},
}

@inproceedings{neurips2022_effective_64229,
  title = {{Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55199},
}

@inproceedings{neurips2023_defending_67546,
  title = {{Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72193},
}

@inproceedings{aaai2017_optimal_100924,
  title = {{Optimal Personalized Defense Strategy Against Man-In-The-Middle Attack}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10565},
}

@inproceedings{aaai2025_ufid_11127,
  title = {{UFID: A Unified Framework for Black-box Input-level Backdoor Detection on Diffusion Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34941},
}

@inproceedings{neurips2023_parafuzz_67342,
  title = {{ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72384},
}

@inproceedings{iclr2023_distilling_32599,
  title = {{Distilling Cognitive Backdoor Patterns within an Image}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/12155},
}

@inproceedings{neurips2024_mitigating_73016,
  title = {{Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94407},
}

@inproceedings{neurips2023_black_68391,
  title = {{Black-box Backdoor Defense via Zero-shot Image Purification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71421},
}

@inproceedings{aaai2025_backdoor_8225,
  title = {{Backdoor Token Unlearning: Exposing and Defending Backdoors in Pretrained Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34605},
}

@inproceedings{aaai2025_two_11119,
  title = {{Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34441},
}

@inproceedings{cvpr2024_attack_22789,
  title = {{Attack To Defend: Exploiting Adversarial Attacks for Detecting Poisoned Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024},
  url = {https://cvpr.thecvf.com/virtual/2024/poster/31253},
}

@inproceedings{neurips2023_bird_69280,
  title = {{BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70618},
}

@inproceedings{neurips2024_sampdetox_72666,
  title = {{SampDetox: Black-box Backdoor Defense via Perturbation-based Sample Detoxification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94718},
}

@inproceedings{aaai2025_honeypotnet_9454,
  title = {{HoneypotNet: Backdoor Attacks Against Model Extraction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32872},
}

@inproceedings{cvpr2025_dede_25864,
  title = {{DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/35109},
}

@inproceedings{iclr2024_backdoor_33484,
  title = {{Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19591},
}

@inproceedings{aaai2026_towards_107241,
  title = {{Towards Effective, Stealthy, and Persistent Backdoor Attacks Targeting Graph Foundation Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39593},
}

@inproceedings{neurips2025_defending_76568,
  title = {{Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=KkONOZwvSU},
}

@inproceedings{iclr2025_activation_82374,
  title = {{Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29418},
}

@inproceedings{iclr2026_reliable_88697,
  title = {{Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=q5ePtZc9N7},
}

@inproceedings{aaai2026_transferable_99843,
  title = {{Transferable Backdoor Attacks for Code Models via Sharpness-Aware Adversarial Perturbation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/36964},
}

@inproceedings{aaai2026_dift_100090,
  title = {{DIFT: Protecting Contrastive Learning Against Data Poisoning Backdoor Attacks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/37141},
}

@inproceedings{aaai2026_dup_108014,
  title = {{DUP: Detection-guided Unlearning for Backdoor Purification in Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40366},
}

@inproceedings{aaai2026_purmm_108515,
  title = {{PurMM: Attention-Guided Test-Time Backdoor Purification in Multimodal Large Language Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40867},
}

@inproceedings{icml2025_test_45924,
  title = {{Test-Time Multimodal Backdoor Detection by Contrastive Prompting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46621},
}