Vincent Immler | Searcharxiv

arXiv2025

Hardware-Enabled Mechanisms for Verifying Responsible AI Development

Advancements in AI capabilities, driven in large part by scaling up computing resources used for AI training, have created opportunities to address major global challenges but also pose risks of misuse. Hardware-enabled mechanisms (HEMs) can support responsible AI development by enabling verifiable reporting of key properties of AI training activities such as quantity of compute used, training cluster configuration or location, as well as policy enforcement. Such tools can promote transparency and improve security, while addressing privacy and intellectual property concerns. Based on insights from an interdisciplinary workshop, we identify open questions regarding potential implementation approaches, emphasizing the need for further research to ensure robust, scalable solutions.

cs.CR↗

arXiv2023

On the Impact of the Hardware Warm-Up Time on Deep Learning-Based RF Fingerprinting

Deep learning-based RF fingerprinting offers great potential for improving the security robustness of various emerging wireless networks. Although much progress has been done in enhancing fingerprinting methods, the impact of device hardware stabilization and warm-up time on the achievable fingerprinting performances has not received adequate attention. As such, this paper focuses on addressing this gap by investigating and shedding light on what could go wrong if the hardware stabilization aspects are overlooked. Specifically, our experimental results show that when the deep learning models are trained with data samples captured after the hardware stabilizes but tested with data captured right after powering on the devices, the device classification accuracy drops below 37%. However, when both the training and testing data are captured after the stabilization period, the achievable average accuracy exceeds 99%, when the model is trained and tested on the same day, and achieves 88% and 96% when the model is trained on one day but tested on another day, for the wireless and wired scenarios, respectively. Additionally, in this work, we leverage simulation and testbed experimentation to explain the cause behind the I/Q signal behavior observed during the device hardware warm-up time that led to the RF fingerprinting performance degradation. Furthermore, we release a large WiFi dataset, containing both unstable (collected during the warm-up period) and stable (collected after the warm-up period) captures across multiple days. Our work contributes datasets, explanations, and guidelines to enhance the robustness of RF fingerprinting in securing emerging wireless networks.

cs.CR↗