“Stealthy Location Tracking with Ninja Codes” by Imoto, Kato and Takeuchi
Conference:
Experience Type(s):
Title:
- Stealthy Location Tracking with Ninja Codes
Organizer(s)/Presenter(s):
Description:
In this paper we describe Ninja Codes, neurally-generated fiducial markers that blend naturally into real-world environments. Taking inspiration from recent advances in deep steganography, we train a neural network that transforms arbitrary images into functional fiducial markers (Ninja Codes) with minimal visual changes. Ninja Codes can be pasted onto various surfaces, to provide location tracking capability for applications such as augmented reality, robotics, etc. Ninja Codes can be printed using common color printers on regular paper, and can be detected by any hardware equipped with a standard RGB camera and capable of running inference.
References:
[1] Shumeet Baluja. 2017. Hiding Images in Plain Sight: Deep Steganography. In Proceedings of NeurIPS 2017. 2066–2076.
[2] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. 2014. Describing Textures in the Wild. In Proceedings of CVPR 2014. 3606–3613.
[3] Mark Fiala. 2005. A Fiducial Marker System Using Digital Techniques. In Proceedings of CVPR 2005. 590–596.
[4] Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár. 2014. Microsoft COCO: Common Objects in Context. In Proceedings of ECCV 2014. 740–755.
[5] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Cheng-Yang Fu Scott Reed, and Alexander C. Berg. 2016. SSD: Single Shot MultiBox Detector. In Proceedings of ECCV 2016. 21–37.
[6] Olaf Ronneberger, Philipp Fischer, and Thomaas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In Proceedings of MICCAI 2015. 234–241.
[7] Matthew Tancik, Ben Mildenhall, and Ren Ng.2020. StegaStamp: Invisible Hyperlinks in Physical Photographs. In Proceedings of CVPR 2020. 2117–2126.
[8] Mustafa B. Yaldiz, Andreas Meuleman, Hyeonjoong Jang, Hyunho Ha, and Min H. Kim. 2021. DeepFormableTag: End-to-end Generation and Recognition of Deformable Fiducial Markers. ACM Transactions on Graphics 40, 4, Article 67 (2021).
[9] Jiren Zhu, Russel Kaplan, Justin Johnson, and Fei-Fei Li. 2018. HiDDeN: Hiding Data with Deep Networks. In Proceedings of ECCV 2018. 657–672.
