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

TL;DR

VisCode is a deep learning-based method that embeds user-specified data into visualization images without distortion, enabling secure and robust data encoding for visualization applications.

Contribution

The paper introduces a novel deep neural network framework for embedding information into visualization images, considering visual saliency and layout optimization for large-scale encoding.

Findings

High decoding success rate demonstrated

Robust against data attacks and distortions

Efficient encoding and decoding performance

Abstract

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.