Markus Weber

Steffen Becker, René Walendy, Markus Weber et al.

Hardware Reverse Engineering (HRE) is a technique for analyzing integrated circuits. Experts employ HRE for security-critical tasks, like detecting Trojans or intellectual property violations, relying not only on their experience and customized tools but also on their cognitive abilities. In this work, we introduce ReverSim, a software environment that models key HRE subprocesses and integrates standardized cognitive tests. ReverSim enables quantitative studies with easier-to-recruit non-experts to uncover cognitive factors relevant to HRE. We empirically evaluated ReverSim in three studies. Semi-structured interviews with 14 HRE professionals confirmed its comparability to real-world HRE processes. Two online user studies with 170 novices and intermediates revealed effective differentiation of participant performance across a spectrum of difficulties, and correlations between participants' cognitive processing speed and task performance. ReverSim is available as open-source software, providing a robust platform for controlled experiments to assess cognitive processes in HRE, potentially opening new avenues for hardware protection.

Felix Nobis, Maximilian Geisslinger, Markus Weber et al.

Object detection in camera images, using deep learning has been proven successfully in recent years. Rising detection rates and computationally efficient network structures are pushing this technique towards application in production vehicles. Nevertheless, the sensor quality of the camera is limited in severe weather conditions and through increased sensor noise in sparsely lit areas and at night. Our approach enhances current 2D object detection networks by fusing camera data and projected sparse radar data in the network layers. The proposed CameraRadarFusionNet (CRF-Net) automatically learns at which level the fusion of the sensor data is most beneficial for the detection result. Additionally, we introduce BlackIn, a training strategy inspired by Dropout, which focuses the learning on a specific sensor type. We show that the fusion network is able to outperform a state-of-the-art image-only network for two different datasets. The code for this research will be made available to the public at: https://github.com/TUMFTM/CameraRadarFusionNet.