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

Finding needles in a haystack: A Black-Box Approach to Invisible  Watermark Detection

Multi-label text classification (MLTC) is the task of assigning multiple
labels to a given text, and has a wide range of application domains. Most
existing approaches require an enormous amount of annotated data to learn a
classifier and/or a set of well-defined constraints on the label space
structure, such as hierarchical relations which may be complicated to provide
as the number of labels increases. In this paper, we study the MLTC problem in
annotation-free and scarce-annotation settings in which the magnitude of
available supervision signals is linear to the number of labels. Our method
follows three steps, (1) mapping input text into a set of preliminary label
likelihoods by natural language inference using a pre-trained language model,
(2) calculating a signed label dependency graph by label descriptions, and (3)
updating the preliminary label likelihoods with message passing along the label
dependency graph, driven with a collective loss function that injects the
information of expected label frequency and average multi-label cardinality of
predictions. The experiments show that the proposed framework achieves
effective performance under low supervision settings with almost imperceptible
computational and memory overheads added to the usage of pre-trained language
model outperforming its initial performance by 70\% in terms of example-based
F1 score.

通过使用预训练语言模型，在低监督情况下有效提高多标签文本分类性能的方法。