DeepMorph: A System for Hiding Bitstrings in Morphable Vector Drawings

TL;DR

DeepMorph is a neural network-based system that embeds and recovers digital bitstrings within vector drawings, enabling artistic information transfer with robustness to real-world conditions and applications in augmented reality.

Contribution

The paper introduces DeepMorph, a novel neural network approach for embedding and decoding digital information in vector drawings with robustness and artistic flexibility.

Findings

Reliable recovery of bitstrings from real-world photos

Effective embedding in vector graphics with artistic freedom

Robust decoding in complex scenes and augmented reality

Abstract

We introduce DeepMorph, an information embedding technique for vector drawings. Provided a vector drawing, such as a Scalable Vector Graphics (SVG) file, our method embeds bitstrings in the image by perturbing the drawing primitives (lines, circles, etc.). This results in a morphed image that can be decoded to recover the original bitstring. The use-case is similar to that of the well-known QR code, but our solution provides creatives with artistic freedom to transfer digital information via drawings of their own design. The method comprises two neural networks, which are trained jointly: an encoder network that transforms a bitstring into a perturbation of the drawing primitives, and a decoder network that recovers the bitstring from an image of the morphed drawing. To enable end-to-end training via back propagation, we introduce a soft rasterizer, which is differentiable with respect to perturbations of the drawing primitives. In order to add robustness towards real-world image capture conditions, image corruptions are injected between the soft rasterizer and the decoder. Further, the addition of an object detection and camera pose estimation system enables decoding of drawings in complex scenes as well as use of the drawings as markers for use in augmented reality applications. We demonstrate that our method reliably recovers bitstrings from real-world photos of printed drawings, thereby providing a novel solution for creatives to transfer digital information via artistic imagery.