A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks

• Published in: Future internet Vol. 16; no. 11; p. 390
• Main Authors: Liu, Xinyun, Xu, Ronghua, Chen, Yu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Accident investigations; Artificial intelligence; Authenticity; Big Data; Blockchain; Cybersecurity; Data acquisition; Data exchange; Data integrity; Deep learning; Digital imaging; Digital signatures; Digital watermarking; Digital watermarks; Distributed ledger; Feasibility studies; Forensic sciences; Frames (data processing); Generative adversarial networks; Image enhancement; Information storage and retrieval; Intelligent Transportation System (ITS); Intelligent transportation systems; Internet of Vehicles; Internet of Vehicles (IoVs); Network security; Noise; Performance evaluation; security; Social networks; Storage; Traffic accidents & safety; Traffic safety; Transportation networks; Transportation services; Vehicles; Video communication; Video data; United States; New Jersey
• DOI: 10.3390/fi16110390
• ISSN: 1999-5903

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. 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). 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. 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.