# Blind Visual Motif Removal from a Single Image

arXiv: https://arxiv.org/abs/1904.02756 · HTML: https://mlsanity.com/paper/1904.02756/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 18 of 18)

- [Invisible Image Watermarks Are Provably Removable Using Generative AI](https://mlsanity.com/paper/2306.01953/) (2023)
- [Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization](https://mlsanity.com/paper/2109.05750/) (2021)
- [Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal](https://mlsanity.com/paper/2207.08178/) (2022)
- [ShaDocNet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal](https://mlsanity.com/paper/2211.16675/) (2022)
- [Visible Watermark Removal via Self-calibrated Localization and Background Refinement](https://mlsanity.com/paper/2108.03581/) (2021)
- [Simulating analogue film damage to analyse and improve artefact restoration on high-resolution scans](https://mlsanity.com/paper/2302.10004/) (2023)
- [Hierarchical Contrastive Learning for Pattern-Generalizable Image Corruption Detection](https://mlsanity.com/paper/2308.14061/) (2023)
- [Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal](https://mlsanity.com/paper/2312.14383/) (2023)
- [Image Harmonization with Region-wise Contrastive Learning](https://mlsanity.com/paper/2205.14058/) (2022)
- [Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking](https://mlsanity.com/paper/2303.09858/) (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)
- [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)
- [ARTeFACT: Benchmarking Segmentation Models on Diverse Analogue Media Damage](https://mlsanity.com/paper/2412.04580/) (2024)
- [Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal](https://mlsanity.com/paper/2504.04687/) (2025)
- [SSA-3DGS: Unsupervised Removal of Screen-Space Artifacts for 3D Gaussian Splatting](https://mlsanity.com/paper/2607.05598/) (2026)
- [Context-Aware Mutual Learning for Blind Image Inpainting and Beyond](https://mlsanity.com/paper/2609.14439/) (2026)

## Abstract (arXiv)

Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image. For example, decorative text that specifies where the image was taken, repeatedly appears across a variety of different images. Often, the reoccurring visual motif, is semantically similar, yet, differs in location, style and content (e.g. text placement, font and letters). This work proposes a deep learning based technique for blind removal of such objects. In the blind setting, the location and exact geometry of the motif are unknown. Our approach simultaneously estimates which pixels contain the visual motif, and synthesizes the underlying latent image. It is applied to a single input image, without any user assistance in specifying the location of the motif, achieving state-of-the-art results for blind removal of both opaque and semi-transparent visual motifs.