Visible watermarks, while instrumental in protecting image copyrights,
frequently distort the underlying content, complicating tasks like scene
interpretation and image editing. Visible watermark removal aims to eliminate
the interference of watermarks and restore the background content. However,
existing methods often implement watermark component removal and background
restoration tasks within a singular branch, leading to residual watermarks in
the predictions and ignoring cases where watermarks heavily obscure the
background. To address these limitations, this study introduces the Removing
Interference and Recovering Content Imaginatively (RIRCI) framework. RIRCI
embodies a two-stage approach: the initial phase centers on discerning and
segregating the watermark component, while the subsequent phase focuses on
background content restoration. To achieve meticulous background restoration,
our proposed model employs a dual-path network capable of fully exploring the
intrinsic background information beneath semi-transparent watermarks and
peripheral contextual information from unaffected regions. Moreover, a Global
and Local Context Interaction module is built upon multi-layer perceptrons and
bidirectional feature transformation for comprehensive representation modeling
in the background restoration phase. The efficacy of our approach is
empirically validated across two large-scale datasets, and our findings reveal
a marked enhancement over existing watermark removal techniques.

利用 Removing Interference and Recovering Content Imaginatively（RIRCI）框架，本研究提出了一种二阶段方法，旨在消除可见水印干扰并恢复背景内容，通过使用双通道网络和全局与局部上下文交互模块，在背景恢复阶段可以充分探索半透明水印下的本质背景信息和未受影响区域的外部环境信息，从而在现有水印去除技术基础上取得显著提升。

去除干扰并巧妙恢复内容的可见水印消除方法

Removing Interference and Recovering Content Imaginatively for Visible  Watermark Removal

Due to costly efforts during data acquisition and model training, Deep Neural
Networks (DNNs) belong to the intellectual property of the model creator.
Hence, unauthorized use, theft, or modification may lead to legal
repercussions. Existing DNN watermarking methods for ownership proof are often
non-intuitive, embed human-invisible marks, require trust in algorithmic
assessment that lacks human-understandable attributes, and rely on rigid
thresholds, making it susceptible to failure in cases of partial watermark
erasure.
This paper introduces ClearMark, the first DNN watermarking method designed
for intuitive human assessment. ClearMark embeds visible watermarks, enabling
human decision-making without rigid value thresholds while allowing
technology-assisted evaluations. ClearMark defines a transposed model
architecture allowing to use of the model in a backward fashion to interwove
the watermark with the main task within all model parameters. Compared to
existing watermarking methods, ClearMark produces visual watermarks that are
easy for humans to understand without requiring complex verification algorithms
or strict thresholds. The watermark is embedded within all model parameters and
entangled with the main task, exhibiting superior robustness. It shows an
8,544-bit watermark capacity comparable to the strongest existing work.
Crucially, ClearMark's effectiveness is model and dataset-agnostic, and
resilient against adversarial model manipulations, as demonstrated in a
comprehensive study performed with four datasets and seven architectures.

ClearMark 是第一种设计用于直观人工评估的 DNN 水印方法，通过在模型参数中嵌入可见水印，以允许人工决策，同时允许技术辅助评估。相较于现有的水印方法，ClearMark 生成的可视化水印易于人类理解，无需复杂的验证算法或严格的阈值。水印嵌入在所有模型参数中，与主要任务交织在一起，表现出卓越的鲁棒性。ClearMark 在四个数据集和七种架构的综合研究中证明了其在不同模型和数据集上的普适性和对抗模型篡改的强韧性。

ClearMark：通过转置模型训练的直观且鲁棒性强的模型水印技术

ClearMark: Intuitive and Robust Model Watermarking via Transposed Model  Training

Visible watermarks are widely-used in images to protect copyright ownership.
Analyzing watermark removal helps to reinforce the anti-attack techniques in an
adversarial way. Current removal methods normally leverage image-to-image
translation techniques. Nevertheless, the uncertainty of the size, shape, color
and transparency of the watermarks set a huge barrier for these methods. To
combat this, we combine traditional watermarked image decomposition into a
two-stage generator, called Watermark-Decomposition Network (WDNet), where the
first stage predicts a rough decomposition from the whole watermarked image and
the second stage specifically centers on the watermarked area to refine the
removal results. The decomposition formulation enables WDNet to separate
watermarks from the images rather than simply removing them. We further show
that these separated watermarks can serve as extra nutrients for building a
larger training dataset and further improving removal performance. Besides, we
construct a large-scale dataset named CLWD, which mainly contains colored
watermarks, to fill the vacuum of colored watermark removal dataset. Extensive
experiments on the public gray-scale dataset LVW and CLWD consistently show
that the proposed WDNet outperforms the state-of-the-art approaches both in
accuracy and efficiency. The code and CLWD dataset are publicly available at
this https URL

该研究提出了一种名为 Watermark-Decomposition Network（WDNet）的二阶段生成器，旨在分离图片中的水印，而不仅仅是简单地删除它们，并通过构建一个命名为 CLWD 的大规模数据集来显示 WDNet 在可见水印识别和去除性能上的优越性