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

Current 3D single object tracking methods are typically based on VoteNet, a
3D region proposal network. Despite the success, using a single seed point
feature as the cue for offset learning in VoteNet prevents high-quality 3D
proposals from being generated. Moreover, seed points with different importance
are treated equally in the voting process, aggravating this defect. To address
these issues, we propose a novel global-local transformer voting scheme to
provide more informative cues and guide the model pay more attention on
potential seed points, promoting the generation of high-quality 3D proposals.
Technically, a global-local transformer (GLT) module is employed to integrate
object- and patch-aware prior into seed point features to effectively form
strong feature representation for geometric positions of the seed points, thus
providing more robust and accurate cues for offset learning. Subsequently, a
simple yet effective training strategy is designed to train the GLT module. We
develop an importance prediction branch to learn the potential importance of
the seed points and treat the output weights vector as a training constraint
term. By incorporating the above components together, we exhibit a superior
tracking method GLT-T. Extensive experiments on challenging KITTI and NuScenes
benchmarks demonstrate that GLT-T achieves state-of-the-art performance in the
3D single object tracking task. Besides, further ablation studies show the
advantages of the proposed global-local transformer voting scheme over the
original VoteNet. Code and models will be available at
this https URL

本研究提出了一种新的全局局部 Transformer 投票方案，以提供更多信息提示，引导模型更多地关注潜在的种子点，从而促进高质量的 3D 提案的生成，并实验表明该方法在 3D 单对象跟踪任务中具有最先进的性能。