【推荐理由】该方法的目标是为所有生成的图像隐藏一个不可见的水印，以便未来进行检测和/或识别。该方法可以快速微调图像生成器的潜在解码器。
The Stable Signature: Rooting Watermarks in Latent Diffusion Models
Pierre Fernandez, Guillaume Couairon, Herve J ´ egou ´, Matthijs Douze, Teddy Furon
[Meta AI & Centre Inria de l’Universite de Rennes & Sorbonne University]
【论文链接】https://arxiv.org/pdf/2303.15435.pdf
【摘要】生成图像建模可以实现广泛的应用，但也会引起有关负责任部署的伦理问题。本论文介绍了一种积极的策略，结合图像水印和潜在扩散模型。其目标是让所有生成的图像都隐藏一个不可见的水印，以便未来进行检测和/或识别。该方法快速微调图像生成器的潜在解码器，条件是一个二进制签名。预先训练的水印提取器从任何生成的图像中恢复隐藏的签名，然后统计测试确定它是否来自生成模型。本文评估了水印在各种生成任务中的隐形性和稳健性，结果显示Stable Signature即使在图像被修改后仍然有效。例如，它可以检测来自文本提示生成的图像的来源，然后将其裁剪以保留10％的内容，在误报率低于10^-6的情况下，准确率高达90％以上。
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