Abstract
Trust and security are fundamental to the successful adoption of the Internet of Things (IoT). This paper proposes a secure message authentication scheme based on steganographic secret sharing for building trust in IoT systems. In our scheme, the message is split and distributed to two participants by a dealer, and it can be revealed only when the two authorized participants grant their consents. Neither of the participants can disclose the message without the consent of the other. To avoid malicious cyberattacks in IoT communications, each share of message is concealed in the form of a human face image, referred to as the shadow image, via a generative adversarial network (GAN). For each participant, a convolutional neural network (CNN) is trained to extract the share of message from the shadow image generated with the participant’s key. Distortions and alterations to the shadow images may occur during the transmission from the dealer to the participant. As a tamper-evident design, each shadow image is morphed with the participant’s source image under the participant’s customized morphing parameter. Cheater detection is also crucial for involved participants to identify fake shadow images during the secret retrieval process. As a cheating countermeasure, the shadow image for one participant is morphed with that for the other and then morphed further with the given source image. The proposed scheme enables multi-factor authentication in the sense that the message is protected by the keys, source images, and morphing parameters of two participants.
Highlights
With the development of wireless networks, the Internet of Things (IoT) has become an important infrastructure in an intelligent society
We first report the experimental results of the face images generator using the PGGAN, and an extractor is trained based on the pre-trained generator
Based on the pre-trained generator and extractor, we evaluate the performance of the secret sharing scheme by simulation experiments, which include the generation of the shadow images and the corresponding authentication images
Summary
With the development of wireless networks, the Internet of Things (IoT) has become an important infrastructure in an intelligent society. As a common information security technique, secret sharing was first proposed by Shamir [1] and Blakley [2] and has been studied for the secure transmission or storage of secret messages in cloud-based IoT systems [3]–[5], in which the cloud server is taken as the. Duo to the development of steganography with deep learning, we expect deep learning to be introduced into image-based secret sharing to improve the security of the secret shares. We propose a secret sharing scheme via deep learning-based steganography and image morphing technique, which takes face images as cover images.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
