Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study

Computer Science > Cryptography and Security [Submitted on 14 May 2026] Model Forensics in AI-Native Wireless Networks: Taxonomy, Applications, and Case Study Pengyu Chen, Weiyang Li, Jin Xu, Jiacheng Wang, Ning Wang, Dusit Niyato, Tao Xiang As artificial intelligence (AI) is increasingly embedded in wireless networks, models are becoming core components that influence signal processing, resource scheduling and network control. However, model anomalies, tampering and malicious functions also introduce new security risks. In this article, we focus on model forensics in AI-native wireless networks. Specifically, we first discuss key problems including model authenticity verification, malicious function identification and accountability tracing, and summarize the main categories of model forensics. We then explain the role of model forensics in AI-native wireless networks and review representative application scenarios. In the case study, we use RF fingerprinting as an example and present two concrete workflows based on watermark authentication and backdoor detection, illustrating how provenance authentication and malicious behavior identification can be implemented in practice. The results show that model forensics can provide important support for anomaly assessment, provenance tracing and trustworthy operation in AI-native wireless networks. Finally, we outline several promising directions for future research in this emerging area. Subjects: Cryptography and Security (cs.CR); Signal Processing (eess.SP) Cite as: arXiv:2605.14387 [cs.CR]   (or arXiv:2605.14387v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.14387 Focus to learn more Submission history From: Pengyu Chen [view email] [v1] Thu, 14 May 2026 05:11:09 UTC (13,301 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs eess eess.SP References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)