Information hiding based on Augmented Reality

Chuanlong Li; Xingming Sun; Yuqian Li; Chuanlong Li; Xingming Sun; Yuqian Li

doi:10.3934/mbe.2019240

Mathematical Biosciences and Engineering

2019, Volume 16, Issue 5: 4777-4787. doi: 10.3934/mbe.2019240

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Research article Special Issues

Information hiding based on Augmented Reality

1.
School of Computer and Software, Nanjing University of Information Science and Technology, Ning Liu Road, No. 219, Nanjing, 210044, China
2.
Jiangsu Engineering Centre of Network Monitoring, Ning Liu Road, No. 219, Nanjing, 210044, China
3.
School of Earth and Environmental Sciences, University of Manchester, Oxford Road, Manchester, M13 9PY, United Kingdom

Received: 31 January 2019 Accepted: 09 May 2019 Published: 27 May 2019

Information hiding aims to achieve secret communication via certain carrier. However, these carrier-based methods often have different kinds of deficiencies. In order to solve the problems addressed by the traditional information hiding methods such as the difficult balance between secret embedding rate and detection rate, this paper proposes a novel approach which utilizes Augmented Reality (AR) to achieve secret communication. In this paper, we present an AR based information hiding architecture which combines information hiding, augmented reality, and deep learning methods altogether. The proposed architecture basically follows the idea of secret-key matching policy. The secret sender first maps the secret message to objects, images or coordinates, etc. The mapped objects, images or coordinates then serve as the secret key for further secret revealing. The secret key and concealing model are shared between two communication parties instead of direct transmitting the secret messages. Different secret keys can be combined in order to generate more mapping sequences. Also, deep learning based models are integrated in the architecture to extend the mapping varieties. By taking advantage of the augmented reality technique, the secret messages can be transmitted in various formats which results in higher secret embedding rate in potential. Furthermore, the proposed architecture can be seen as a useful application of coverless information hiding scheme. The experimental system realizes the proposed architecture by implementing convolutional neural network (CNN) based real-time object detection, image recognition, augmented reality and secret-key matching altogether which shows great promise in practice.
- Augmented Reality,
- CNN,
- steganography,
- information hiding,
- deep learning,
- coverless
Citation: Chuanlong Li, Xingming Sun, Yuqian Li. Information hiding based on Augmented Reality[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4777-4787. doi: 10.3934/mbe.2019240

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Abstract

Information hiding aims to achieve secret communication via certain carrier. However, these carrier-based methods often have different kinds of deficiencies. In order to solve the problems addressed by the traditional information hiding methods such as the difficult balance between secret embedding rate and detection rate, this paper proposes a novel approach which utilizes Augmented Reality (AR) to achieve secret communication. In this paper, we present an AR based information hiding architecture which combines information hiding, augmented reality, and deep learning methods altogether. The proposed architecture basically follows the idea of secret-key matching policy. The secret sender first maps the secret message to objects, images or coordinates, etc. The mapped objects, images or coordinates then serve as the secret key for further secret revealing. The secret key and concealing model are shared between two communication parties instead of direct transmitting the secret messages. Different secret keys can be combined in order to generate more mapping sequences. Also, deep learning based models are integrated in the architecture to extend the mapping varieties. By taking advantage of the augmented reality technique, the secret messages can be transmitted in various formats which results in higher secret embedding rate in potential. Furthermore, the proposed architecture can be seen as a useful application of coverless information hiding scheme. The experimental system realizes the proposed architecture by implementing convolutional neural network (CNN) based real-time object detection, image recognition, augmented reality and secret-key matching altogether which shows great promise in practice.

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