Version 1
: Received: 14 September 2024 / Approved: 16 September 2024 / Online: 18 September 2024 (05:22:57 CEST)
How to cite:
Liu, X.; Xu, R.; Chen, Y. A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks. Preprints2024, 2024091228. https://doi.org/10.20944/preprints202409.1228.v1
Liu, X.; Xu, R.; Chen, Y. A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks. Preprints 2024, 2024091228. https://doi.org/10.20944/preprints202409.1228.v1
Liu, X.; Xu, R.; Chen, Y. A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks. Preprints2024, 2024091228. https://doi.org/10.20944/preprints202409.1228.v1
APA Style
Liu, X., Xu, R., & Chen, Y. (2024). A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks. Preprints. https://doi.org/10.20944/preprints202409.1228.v1
Chicago/Turabian Style
Liu, X., Ronghua Xu and Yu Chen. 2024 "A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks" Preprints. https://doi.org/10.20944/preprints202409.1228.v1
Abstract
Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication, the concept of the Internet of Vehicles (IoV) combined with Artificial Intelligence (AI) and big data technologies promotes the vision of an Intelligent Transportation System (ITS). By enabling a comprehensive data exchange platform, ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). Specifically, D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder-noise-decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. Then, BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.
Keywords
Intelligent Transportation System (ITS); Internet of Vehicles (IoV); Digital Watermarking; Deep Learning; Blockchain; Security
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
