Watermark Retrieval from 3D Printed Objects via Convolutional Neural Networks

2018/11/19 by Xin Zhang, Zhang, Xin, Qian Wang +5 · 2 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Steganography and Watermarking Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #QR Code Applications and Technologies

paper · pdf · doi:10.48550/arxiv.1811.07640

openalex publication_date 2018/11/19 · openalex created_date 2018/11/29 · openalex updated_date 2026/07/28

Abstract

We present a method for reading digital data embedded in planar 3D printed surfaces. The data are organised in binary arrays and embedded as surface textures in a way inspired by QR codes. At the core of the retrieval method lies a Convolutional Neural Network, outputting a confidence map of the location of the surface textures encoding value 1 bits. Subsequently, the bit array is retrieved through a series of simple image processing and statistical operations applied on the confidence map. Extensive experimentation with images captured from various camera views, under various illumination conditions and from objects printed with various material colours, shows that the proposed method generalizes well and achieves the level of accuracy required in practical applications.

Citations

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition

Cited by

• A study of the effect of the illumination model on the generation of synthetic training datasets
• Watermark retrieval from 3D printed objects via synthetic data training

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