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.Comment: IEEE VIS 202