In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary watermarks within a given reference dataset using a clean non-watermarked dataset as a reference, without relying on specific decoding methods or prior knowledge of the watermarking techniques. We develop WMD using foundations of offset learning, where a clean non-watermarked dataset enables us to isolate the influence of only watermarked samples in the reference dataset. Our comprehensive evaluations demonstrate the effectiveness of WMD, significantly outperforming naive detection methods, which only yield AUC scores around 0.5. In contrast, WMD consistently achieves impressive detection AUC scores, surpassing 0.9 in most single-watermark datasets and exceeding 0.7 in more challenging multi-watermark scenarios across diverse datasets and watermarking methods. As invisible watermarks become increasingly prevalent, while specific decoding techniques remain undisclosed, our approach provides a versatile solution and establishes a path toward increasing accountability, transparency, and trust in our digital visual content.

本文提出了水印检测 (WMD) 方法，它是第一个在黑盒和无注释条件下进行隐形水印检测的方法。WMD能够在给定的参考数据集中检测任意水印，使用一个未带水印的干净数据集作为参考，而不依赖于特定的解码方法或水印技术的先验知识。我们使用偏移学习的基础原理开发了WMD，其中一个未带水印的干净数据集使我们能够隔离只有带水印样本在参考数据集中的影响。我们进行了全面评估，结果表明WMD的有效性显著优于简单的检测方法，后者只能得到大约0.5的AUC分数。相比之下，WMD在大多数单一水印数据集中 consistently 达到令人印象深刻的检测AUC分数，超过0.9，在更具挑战性的多水印场景中超过0.7，适用于各种数据集和水印方法。随着隐形水印的普及，而特定解码技术仍未公开，我们的方法提供了一种多功能解决方案，并为增加我们数字视觉内容的问责、透明度和信任铺平了道路。

在一个草堆中找到针：隐形水印检测的黑盒方法