# VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network

arXiv: https://arxiv.org/abs/2009.03817 · HTML: https://mlsanity.com/paper/2009.03817/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [Visualization Literacy of Multimodal Large Language Models: A Comparative Study](https://mlsanity.com/paper/2407.10996/) (2024)
- [InvVis: Large-Scale Data Embedding for Invertible Visualization](https://mlsanity.com/paper/2307.16176/) (2023)
- [Explainable XR: Understanding User Behaviors of XR Environments using LLM-assisted Analytics Framework](https://mlsanity.com/paper/2501.13778/) (2025)
- [Generalization of CNNs on Relational Reasoning with Bar Charts](https://mlsanity.com/paper/2503.00086/) (2025)
- [VizDefender: Unmasking Visualization Tampering through Proactive Localization and Intent Inference](https://mlsanity.com/paper/2512.18853/) (2025)
- [SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization](https://mlsanity.com/paper/2403.03449/) (2024)
- [VisGuard: Securing Visualization Dissemination through Tamper-Resistant Data Retrieval](https://mlsanity.com/paper/2507.14459/) (2025)
- [Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legend](https://mlsanity.com/paper/2507.20632/) (2025)
- [Imprinto: Enhancing Infrared Inkjet Watermarking for Human and Machine Perception](https://mlsanity.com/paper/2502.17089/) (2025)
- [VizAnchor: Decoding Manipulation Intent from Tampering Visualizations via Dual-Anchor Reasoning](https://mlsanity.com/paper/2608.24535/) (2026)

## Abstract (arXiv)

We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode.