With the increasing adoption of Advanced Driver Assistance Systems (ADAS) in modern cars, the use of vision systems for autonomous vehicles, driving assistance, and in-vehicle entertainment has introduced new risks and attack vectors to existing In-Vehicle Networks (IVNs), thus bringing considerable concerns to the automotive cybersecurity space. Prior works have focused on analyzing functional or partial security aspects of vision systems during ADAS simulation using specialized Automotive Ethernet (AE) equipment or requiring expensive vehicle-in-the-loop setups. These approaches are either inaccessible to independent security researchers or do not offer comprehensive insights to help researchers understand the practical implications of attacks in a realistic car employing Automotive Ethernet IVNs for vision-related use cases. AEVisionLab allows replication of driving test scenarios directly with COTS ECUs and collection of key network performance metrics, facilitating the design, evaluation, and impact analysis of concrete attacks in the laboratory. We demonstrate the capability of AEVisionLab by designing and evaluating concrete attacks scenarios including eavesdropping and hijacking of SOME/IP services, manipulation and delaying video feed, among others. We envision AEVisionLab as a flexible platform for designing and evaluating both attack and mitigation techniques (e.g., intrusion detection) on AE network, which can be easily extended to support other automotive ECUs, machine learning models for ADAS, or sensors for assisted driving.

Abstract Automatic Speech Recognition (ASR) systems have become ubiquitous. They can be found in a variety of form factors and are increasingly important in our daily lives. As such, ensuring that these systems are equitable to different subgroups of the population is crucial. In this paper, we introduce,AequeVox, an automated testing framework for evaluating the fairness of ASR systems.AequeVoxsimulates different environments to assess the effectiveness of ASR systems for different populations. In addition, we investigate whether the chosen simulations are comprehensible to humans. We further propose a fault localization technique capable of identifying words that are not robust to these varying environments. Both components ofAequeVoxare able to operate in the absence of ground truth data. We evaluateAequeVoxon speech from four different datasets using three different commercial ASRs. Our experiments reveal that non-native English, female and Nigerian English speakers generate109%,528.5%and156.9%more errors, on average than native English, male and UK Midlands speakers, respectively. Our user study also reveals that 82.9% of the simulations (employed through speech transformations) had a comprehensibility rating above seven (out of ten), with the lowest rating being 6.78. This further validates the fairness violations discovered byAequeVox. Finally, we show that the non-robust words, as predicted by the fault localization technique embodied inAequeVox, show223.8%more errors than the predicted robust words across all ASRs.