  Reversible image conversion (RIC) aims to build a reversible transformation
between specific visual content (e.g., short videos) and an embedding image,
where the original content can be restored from the embedding when necessary.
This work develops Invertible Image Conversion Net (IICNet) as a generic
solution to various RIC tasks due to its strong capacity and task-independent
design. Unlike previous encoder-decoder based methods, IICNet maintains a
highly invertible structure based on invertible neural networks (INNs) to
better preserve the information during conversion. We use a relation module and
a channel squeeze layer to improve the INN nonlinearity to extract cross-image
relations and the network flexibility, respectively. Experimental results
demonstrate that IICNet outperforms the specifically-designed methods on
existing RIC tasks and can generalize well to various newly-explored tasks.
With our generic IICNet, we no longer need to hand-engineer task-specific
embedding networks for rapidly occurring visual content. Our source codes are
available at: https://github.com/felixcheng97/IICNet.
