Finding Needles in a Haystack: A Black-Box Approach to Invisible Watermark Detection

Minzhou Pan, Zhenting Wang, Xin Dong, Vikash Sehwag, Lingjuan Lyu, Xue Lin

Abstract: In this paper, we propose WaterMark Detector (\(\textsc {WMD}\)), the first invisible watermark detection method under a black-box and annotation-free setting. \(\textsc {WMD}\) is capable of detecting arbitrary watermarks within a given detection 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 \(\textsc {WMD}\) 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 \(\textsc {WMD}\), which significantly outperforms naive detection methods with AUC scores around only 0.5. In contrast, \(\textsc {WMD}\) 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.