FontCode: Embedding Information in Text Documents using Glyph Perturbation
Chang Xiao, Cheng Zhang, Changxi Zheng
ACM Transaction on Graphics (Presented at SIGGRAPH 2018)
Abstract

We introduce FontCode, an information embedding technique for text documents. Provided a text document with specific fonts, our method embeds user-specified information in the text by perturbing the glyphs of text characters while preserving the text content. We devise an algorithm to choose unobtrusive yet machine-recognizable glyph perturbations, leveraging a recently developed generative model that alters the glyphs of each character continuously on a font manifold. We then introduce an algorithm that embeds a user-provided message in the text document and produces an encoded document whose appearance is minimally perturbed from the original document. We also present a glyph recognition method that recovers the embedded information from an encoded document stored as a vector graphic or pixel image, or even on a printed paper. In addition, we introduce a new error-correction coding scheme that rectifies a certain number of recognition errors. Lastly, we demonstrate that our technique enables a wide array of applications, using it as a text document metadata holder, an unobtrusive optical barcode, a cryptographic message embedding scheme, and a text document signature.

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Acknowledgements

We thank the anonymous reviewers for their constructive feedback. We are grateful to Julie Dorsey for her suggestions of paper revision, Klint Qinami and Michael Falkenstein for video narration. This work was supported in part by the National Science Foundation (CAREER-1453101 and 1717178) and generous donations from SoftBank and Adobe. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies or others.

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