ALIEN: 基于可控生成的分析潜在水印
ALIEN: Analytic Latent Watermarking for Controllable Generation
AI总结：
ALIEN提出了一种基于可控生成的分析潜在水印方法，通过时间依赖调节系数实现可控的水印嵌入，实验结果显示其在多个质量指标和鲁棒性方面均优于现有方法。
摘要：
水印技术是保护知识产权和减少滥用的一种技术替代方案。现有方法聚焦于优化水印潜在变量以平衡水印鲁棒性和保真度，因为潜在扩散模型（LDMs）被认为是生成任务的强大工具。然而，依赖于计算密集型启发式优化进行迭代信号细化导致训练开销高且容易陷入局部最优。为了解决这些问题，我们提出了一个分析水印框架用于可控生成（ALIEN）。我们开发了首个对时间依赖调节系数的分析推导，该系数指导水印残差的扩散以实现可控的水印嵌入模式。实验结果表明，ALIEN-Q在5个质量指标上比最先进的方法高出33.1%，而ALIEN-R在15种不同条件下比最先进的方法在对抗生成变体和稳定性威胁方面提高了14.0%。代码可在https://anonymous.4open.science/r/ALIEN/上获取。
英文摘要：
Watermarking is a technical alternative to safeguarding intellectual property and reducing misuse. Existing methods focus on optimizing watermarked latent variables to balance watermark robustness and fidelity, as Latent diffusion models (LDMs) are considered a powerful tool for generative tasks. However, reliance on computationally intensive heuristic optimization for iterative signal refinement results in high training overhead and local optima entrapment.To address these issues, we propose an \underline{A}na\underline{l}ytical Watermark\underline{i}ng Framework for Controllabl\underline{e} Generatio\underline{n} (ALIEN). We develop the first analytical derivation of the time-dependent modulation coefficient that guides the diffusion of watermark residuals to achieve controllable watermark embedding pattern.Experimental results show that ALIEN-Q outperforms the state-of-the-art by 33.1\% across 5 quality metrics, and ALIEN-R demonstrates 14.0\% improved robustness against generative variant and stability threats compared to the state-of-the-art across 15 distinct conditions. Code can be available at https://anonymous.4open.science/r/ALIEN/.
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。
