## 6 Conclusions and Future Work  
This paper proposes D2 WaVe, a novel decentralized digit watermarking framework to guarantee the security and auditability of video data in IoV networks. Under the umbrella, an FIAE-GAN model, an end-to-end framework for digital watermark embedding and extraction, has been introduced. FIAE-GAN addresses existing deep learning-based watermarking model limitations by incorporating an FIM and an AEM within the encoder. By integrating the DDS system with the Ethereum blockchain, BloVA, a decentralized video frames authentication scheme, can guarantee the authenticity and integrity of data at rest and in transit. Experimental results demonstrate that the FIAE-GAN model achieves high image quality and outperforms SOTA models in invisibility. The proposed D2 WaVe framework incurs small latency, and it is promising for mitigating threats to data integrity and availability in IoV networks.  
When reporting the current version of the D2 WaVe prototype, our ongoing efforts will focus on several directions. First of all, a complete version of the D2 WaVe platform will be constructed to provide auditable and reliable video data services for real-world IoV applications. Therefore, a more comprehensive evaluation of scalability and security regarding system aspects will be conducted to test more benchmark analyses in practical scenarios. Regarding the limitations of implementing our digital watermarking scheme on practical vehicular computing platforms, we aim to optimize the FIAE-GAN model to enhance its lightweight characteristics. Knowledge distillation and model compression are promising methods to reduce computation time by the encoder and decoder and facilitate the model's deployment in resource-constrained environments, particularly in real-world Internet of Vehicles (IoVs) scenarios. In addition, we plan to extend the model's capabilities to accommodate a broader range of compression formats beyond JPEG and various video types, thereby increasing its versatility and robustness. Last but not least, we envision the potential application of our model in critical areas such as medical imaging and metaverse applications, wherein digital watermarking can play a vital role in ensuring data integrity, auditability, and security.  
Conceptualization, X.L., R.X. and Y.C.; methodology, X.L. and R.X.; software, X.L. and R.X.; validation, X.L., R.X. and Y.C.; formal analysis, X.L. and R.X.; investigation, X.L., R.X. and Y.C.; resources, X.L. and R.X.; data curation, X.L. and R.X.; writing--original draft preparation, X.L. and R.X.; writing--review and editing, X.L., R.X. and Y.C.; visualization, X.L. and R.X.; supervision, R.X.; project administration, R.X.; funding acquisition, R.X. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Michigan Technological University-ICC Rapid Seeding Awards.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ AEM & Attention-Enhanced Module \\ BN & Batch Normalization \\ CAV & Connected Automated Vehicles \\ CCTSDB & Chinese Traffic Sign Detection Benchmark \\ \end{tabular}