Latent Diffusion Models (LDMs) have established themselves as powerful tools in the rapidly evolving field of image generation, capable of producing highly realistic images. However, their widespread adoption raises critical concerns about copyright infringement and the misuse of generated content. Watermarking techniques have emerged as a promising solution, enabling copyright identification and misuse tracing through imperceptible markers embedded in generated images. Among these, latent-based watermarking techniques are particularly promising, as they embed watermarks directly into the latent noise without altering the underlying LDM architecture. In this work, we demonstrate that such latent-based watermarks are practically vulnerable to detection and compromise through systematic analysis of output images' statistical patterns for the first time. To counter this, we propose SWA-LDM (Stealthy Watermark for LDM), a lightweight framework that enhances stealth by dynamically randomizing the embedded watermarks using the Gaussian-distributed latent noise inherent to diffusion models. By embedding unique, pattern-free signatures per image, SWA-LDM eliminates detectable artifacts while preserving image quality and extraction robustness. Experiments demonstrate an average of 20% improvement in stealth over state-of-the-art methods, enabling secure deployment of watermarked generative AI in real-world applications.

翻译：潜在扩散模型（LDMs）在快速发展的图像生成领域已成为强大工具，能够生成高度逼真的图像。然而，其广泛采用引发了关于版权侵权和生成内容滥用的严重关切。水印技术作为一种有前景的解决方案应运而生，通过嵌入生成图像中的不可察觉标记，实现版权识别和滥用溯源。其中，基于潜在空间的水印技术尤其具有潜力，因其可直接在潜在噪声中嵌入水印，而无需改变底层LDM架构。本研究中，我们首次通过系统分析输出图像的统计模式，证明此类基于潜在空间的水印在实际应用中易被检测和破解。为此，我们提出SWA-LDM（面向LDM的隐蔽水印），这是一个轻量级框架，通过利用扩散模型固有的高斯分布潜在噪声动态随机化嵌入水印来增强隐蔽性。通过为每幅图像嵌入独特的无模式签名，SWA-LDM在保持图像质量和提取鲁棒性的同时，消除了可检测的伪影。实验表明，该方法在隐蔽性上较现有最优方法平均提升20%，为水印生成式AI在现实应用中的安全部署提供了可能。