InvVis: Large-Scale Data Embedding for Invertible Visualization

Huayuan Ye, Chenhui Li, Yang Li, Changbo Wang

Room: 109

2023-10-26T04:00:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
InvVis can embed a large amount of data into visualization images, users can decode the embedded data and perform rich exploration, such as redesigning (a) or reconstructing (b) visualizations, rebuilding a visualization dashboard based on the decoded source code and chart data (c), or visualizing volume data based on the decoded raw data and rendering parameters (d).
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Information visualization, information steganography, invertible visualization, invertible neural network.


We present InvVis, a new approach for invertible visualization, which is reconstructing or further modifying a visualization from an image. InvVis allows the embedding of a significant amount of data, such as chart data, chart information, source code, etc., into visualization images. The encoded image is perceptually indistinguishable from the original one. We propose a new method to efficiently express chart data in the form of images, enabling large-capacity data embedding. We also outline a model based on the invertible neural network to achieve high-quality data concealing and revealing. We explore and implement a variety of application scenarios of InvVis. Additionally, we conduct a series of evaluation experiments to assess our method from multiple perspectives, including data embedding quality, data restoration accuracy, data encoding capacity, etc. The result of our experiments demonstrates the great potential of InvVis in invertible visualization.