# Visible Watermark Removal via Self-calibrated Localization and Background Refinement

arXiv: https://arxiv.org/abs/2108.03581 · HTML: https://mlsanity.com/paper/2108.03581/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 49 references that are in the map.
- First posted on arXiv 8 Aug 2021.

## Builds on (most cited first, 30 of 32)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [DehazeNet: An End-to-End System for Single Image Haze Removal](https://mlsanity.com/paper/1601.07661/) (2016)
- [Multi-Scale Progressive Fusion Network for Single Image Deraining](https://mlsanity.com/paper/2003.10985/) (2020)
- [Multi-Scale Boosted Dehazing Network with Dense Feature Fusion](https://mlsanity.com/paper/2004.13388/) (2020)
- [Attentive Generative Adversarial Network for Raindrop Removal from a Single Image](https://mlsanity.com/paper/1711.10098/) (2017)
- [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https://mlsanity.com/paper/1712.02478/) (2017)
- [Densely Connected Pyramid Dehazing Network](https://mlsanity.com/paper/1803.08396/) (2018)
- [NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction](https://mlsanity.com/paper/1801.08297/) (2018)
- [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://mlsanity.com/paper/1512.04412/) (2015)
- [Deep Image Harmonization](https://mlsanity.com/paper/1703.00069/) (2017)
- ["Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors](https://mlsanity.com/paper/1812.00467/) (2018)
- [Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN](https://mlsanity.com/paper/1911.08718/) (2019)
- [Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection](https://mlsanity.com/paper/1708.02001/) (2017)
- [Gated Context Aggregation Network for Image Dehazing and Deraining](https://mlsanity.com/paper/1811.08747/) (2018)
- [ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal](https://mlsanity.com/paper/1908.01323/) (2019)
- [A Modulation Module for Multi-task Learning with Applications in Image Retrieval](https://mlsanity.com/paper/1807.06708/) (2018)
- [WDNet: Watermark-Decomposition Network for Visible Watermark Removal](https://mlsanity.com/paper/2012.07616/) (2020)
- [Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network](https://mlsanity.com/paper/2008.02763/) (2020)
- [Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal](https://mlsanity.com/paper/2012.07007/) (2020)
- [Blind Visual Motif Removal from a Single Image](https://mlsanity.com/paper/1904.02756/) (2019)
- [Residual-Guide Feature Fusion Network for Single Image Deraining](https://mlsanity.com/paper/1804.07493/) (2018)

## Built on by (most cited first, 14 of 14)

- [Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal](https://mlsanity.com/paper/2207.08178/) (2022)
- [Warfare:Breaking the Watermark Protection of AI-Generated Content](https://mlsanity.com/paper/2310.07726/) (2023)
- [Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal](https://mlsanity.com/paper/2312.14383/) (2023)
- [Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking](https://mlsanity.com/paper/2303.09858/) (2023)
- [Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions](https://mlsanity.com/paper/2308.13178/) (2023)
- [Spread them Apart: Towards Robust Watermarking of Generated Content](https://mlsanity.com/paper/2502.07845/) (2025)
- [AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective](https://mlsanity.com/paper/2603.24857/) (2026)
- [Mask-Guided Image Person Removal with Data Synthesis](https://mlsanity.com/paper/2209.14890/) (2022)
- [Visual Watermark Removal Based on Deep Learning](https://mlsanity.com/paper/2302.11338/) (2023)
- [WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning](https://mlsanity.com/paper/2308.10195/) (2023)
- [Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal](https://mlsanity.com/paper/2504.04687/) (2025)
- [Beyond Invisibility: Learning Robust Visible Watermarks for Stronger Copyright Protection](https://mlsanity.com/paper/2506.02665/) (2025)
- [AMIF: Authorizable Medical Image Fusion Model with Built-in Authentication](https://mlsanity.com/paper/2603.24296/) (2026)
- [Context-Aware Mutual Learning for Blind Image Inpainting and Beyond](https://mlsanity.com/paper/2609.14439/) (2026)

## Abstract (arXiv)

Superimposing visible watermarks on images provides a powerful weapon to cope with the copyright issue. Watermark removal techniques, which can strengthen the robustness of visible watermarks in an adversarial way, have attracted increasing research interest. Modern watermark removal methods perform watermark localization and background restoration simultaneously, which could be viewed as a multi-task learning problem. However, existing approaches suffer from incomplete detected watermark and degraded texture quality of restored background. Therefore, we design a two-stage multi-task network to address the above issues. The coarse stage consists of a watermark branch and a background branch, in which the watermark branch self-calibrates the roughly estimated mask and passes the calibrated mask to background branch to reconstruct the watermarked area. In the refinement stage, we integrate multi-level features to improve the texture quality of watermarked area. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method.