你的写作是否被AI模仿？通过隐形水印在创意写作中揭露模仿
Protecting Creative Writing Copyright against AI Imitation via Implicit Watermarking
AI总结：
本文提出WIND方法，通过隐含编码在创意写作中实现可验证的版权保护，有效对抗AI模仿，达到高准确率的验证效果。
摘要：
高效的知识注入方法对于大型语言模型（LLMs）来说，例如上下文学习、知识编辑和高效的参数微调，显著增强了模型在下游任务中的实用性。然而，这些方法也带来了未经授权的模仿和高价值无结构数据资产（如创意作品）的来源问题的严重风险。目前，创意作品的版权保护方法主要集中在视觉艺术上，留下了一个关键且未解决的数据工程挑战，即保护创意写作的安全。在本文中，我们提出了WIND（通过隐含和非破坏性解耦的水印），一种新颖的零水印、可验证和隐含的方案，通过提供可验证的版权保护来保护创意写作数据库。具体来说，我们将创意本质分解为五个关键要素，这些要素通过设计的实例限定机制利用LLMs提取并整合到简化的列表中。这些列表使WIND能够通过隐含编码在解耦的创意空间中将核心版权属性转换为可验证的水印，其中'解耦'指的是将创意特定和创意无关的特征分开。这种方法利用隐含编码，避免了对脆弱文本内容的扭曲。广泛的实验表明，WIND能够有效地验证创意写作的版权所有权以对抗AI模仿，实现了超过98%的F1分数，并在严格低误报率条件下保持稳健的性能，而现有最先进的文本水印方法则难以达到此效果。
英文摘要：
Large language models (LLMs) enable powerful knowledge injection through approaches such as in-context learning and fine-tuning, but they also introduce new risks of unauthorized imitation of high-value creative works. Existing copyright protection techniques mainly focus on visual media, leaving the protection of creative writing largely unexplored. In this work, we investigate a new challenge: verifying whether AI-generated texts inherit the creative essence of protected works without authorization. We propose WIND (Watermarking via Implicit and Non-disruptive Disentanglement), a zero-watermarking framework that constructs an implicit and verifiable creative signature for copyright verification. WIND decomposes creative essence into five complementary dimensions and leverages an LLM-based instance delimitation mechanism to extract condensed representations of protected creative characteristics. By disentangling creative-specific information from irrelevant textual variations, these representations are mapped into a compact watermark space without modifying the original texts. Extensive experiments demonstrate that WIND achieves over 98\% F1 scores while maintaining low false-positive rates, substantially outperforming existing watermarking and text classification approaches under various AI imitation scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
