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

@inproceedings{icml2023_a_42548,
  title = {{A Watermark for Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24544},
}

@inproceedings{aaai2022_protecting_2592,
  title = {{Protecting Intellectual Property of Language Generation APIs with Lexical Watermark}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/10758-protecting-intellectual-property-of-language-generation-apis-with-lexical-watermark/},
}

@inproceedings{aaai2022_tracing_2870,
  title = {{Tracing Text Provenance via Context-Aware Lexical Substitution}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/11613-tracing-text-provenance-via-context-aware-lexical-substitution/},
}

@inproceedings{aaai2024_watermarking_7786,
  title = {{Watermarking Conditional Text Generation for AI Detection: Unveiling Challenges and a Semantic-Aware Watermark Remedy}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29756},
}

@inproceedings{iclr2024_on_33928,
  title = {{On the Reliability of Watermarks for Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19147},
}

@inproceedings{iclr2024_provable_34486,
  title = {{Provable Robust Watermarking for AI-Generated Text}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18588},
}

@inproceedings{icml2023_protecting_42624,
  title = {{Protecting Language Generation Models via Invisible Watermarking}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/24208},
}

@inproceedings{icml2024_watermark_44604,
  title = {{Watermark Stealing in Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33848},
}

@inproceedings{iclr2024_an_34929,
  title = {{An Unforgeable Publicly Verifiable Watermark for Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18145},
}

@inproceedings{iclr2024_a_33693,
  title = {{A Semantic Invariant Robust Watermark for Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19382},
}

@inproceedings{iclr2024_unbiased_35501,
  title = {{Unbiased Watermark for Large Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17572},
}

@inproceedings{icml2024_a_44847,
  title = {{A Resilient and Accessible Distribution-Preserving Watermark for Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33605},
}

@inproceedings{neurips2024_watermarking_74014,
  title = {{Watermarking Makes Language Models Radioactive}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93506},
}

@inproceedings{neurips2024_no_74089,
  title = {{No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93444},
}

@inproceedings{aaai2025_can_8385,
  title = {{Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34684},
}

@inproceedings{icml2024_token_43702,
  title = {{Token-Specific Watermarking with Enhanced Detectability and Semantic Coherence for Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34750},
}

@inproceedings{icml2024_de_44155,
  title = {{DE-COP: Detecting Copyrighted Content in Language Models Training Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34297},
}

@inproceedings{neurips2024_watermax_71449,
  title = {{WaterMax: breaking the LLM watermark detectability-robustness-quality trade-off}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95807},
}

@inproceedings{iclr2024_on_33789,
  title = {{On the Learnability of Watermarks for Language Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19286},
}

@inproceedings{iclr2024_towards_34138,
  title = {{Towards Codable Watermarking for Injecting Multi-Bits Information to LLMs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18937},
}

@inproceedings{icml2024_adaptive_43577,
  title = {{Adaptive Text Watermark for Large Language Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34875},
}

@inproceedings{neurips2024_bileve_74372,
  title = {{Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93192},
}

@inproceedings{iclr2025_permute_82558,
  title = {{Permute-and-Flip: An optimally stable and watermarkable decoder for LLMs}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29239},
}

@inproceedings{aaai2026_eguard_108524,
  title = {{Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40876},
}