| Challenge: | Existing watermarking methods use a target embedding to create watermarks, but this method results in each embeddable having the same component, making it difficult to remove the watermark. |
| Approach: | They propose to use embedding watermarks to protect EaaS from model extraction attacks . eaas is vulnerable to model extraction, highlighting the need for copyright protection . |
| Outcome: | The proposed method can watermark embeddings against model extraction attacks without sacrificing the quality of the embeddables. |
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| Challenge: | Existing EaaS watermarks can be removed by paraphrasing when attackers clone the model. |
| Approach: | They propose a method that integrates a target embedding into the original embeddable based on the presence of trigger words in the input text. |
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WARDEN: Multi-Directional Backdoor Watermarks for Embedding-as-a-Service Copyright Protection (2024.acl-long)
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| Challenge: | Prior studies have shown that EaaS can be prone to model extraction attacks, however, this concern could be mitigated by adding backdoor watermarks to the text embeddings. |
| Approach: | They propose a new method that removes backdoor watermarks while maintaining the high utility of embeddings. |
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Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS Watermark (2025.findings-emnlp)
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| Challenge: | Embedding-as-a-Service (EaaS) is a successful business pattern but faces significant challenges related to various forms of copyright infringement. |
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GuardEmb: Dynamic Watermark for Safeguarding Large Language Model Embedding Service Against Model Stealing Attack (2024.findings-emnlp)
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| Challenge: | Recent studies reveal the risk of the model stealing attack, posing a financial threat to EaaS providers. |
| Approach: | They propose a dynamic embedding watermarking method that detects watermarks in embedded text . this method is a cross-platform approach that trains a verifier to detect watermark . |
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Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark (2023.acl-long)
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Wenjun Peng, Jingwei Yi, Fangzhao Wu, Shangxi Wu, Bin Bin Zhu, Lingjuan Lyu, Binxing Jiao, Tong Xu, Guangzhong Sun, Xing Xie
| Challenge: | Large language models (LLMs) have demonstrated exceptional abilities in both text understanding and generation. |
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Subtle Signatures, Strong Shields: Advancing Robust and Imperceptible Watermarking in Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have led to an increase in AI-generated text on the Internet, presenting a crucial challenge to differentiate AI-created content from human-written text. |
| Approach: | They propose a novel approach to embed watermarks into LLMs that leverages token prior probabilities to improve detectability and maintain watermark imperceptibility. |
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Watermark under Fire: A Robustness Evaluation of LLM Watermarking (2025.findings-emnlp)
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| Challenge: | Various watermarking methods have been proposed to identify LLM-generated texts . lack of unified evaluation platforms has left many critical questions unanswered . |
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Advancing Beyond Identification: Multi-bit Watermark for Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods to detect machine-generated text focus on detection, but some misuses require tracing the adversary user for counteracting them. |
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Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models (2026.findings-acl)
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| Challenge: | Existing detection methods for large language models rely on fixed strategies to steal watermarks. |
| Approach: | They propose a novel steal-based watermark algorithm that derives watermark information from watermarked texts to craft highly targeted adversarial attacks. |
| Outcome: | The proposed system significantly increases steal efficiency against target watermarks under identical conditions. |
IDEAW: Robust Neural Audio Watermarking with Invertible Dual-Embedding (2024.emnlp-main)
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| Challenge: | Traditional methods for embedding watermarks into audio have low capacity and unsatisfactory imperceptibility. |
| Approach: | They propose a dual-embedding wa- termarking model for efficient locating and a model that can withstand attacks. |
| Outcome: | The proposed model can withstand attacks with higher capacity and more efficient locating ability compared to existing methods. |