  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.
