PHANTOM: Anamorphic Art-Based Attacks Disrupt Connected Vehicle Mobility
Analysis
This research introduces PHANTOM, a novel attack framework leveraging anamorphic art to create perspective-dependent adversarial examples that fool object detectors in connected autonomous vehicles (CAVs). The key innovation lies in its black-box nature and strong transferability across different detector architectures. The high success rate, even in degraded conditions, highlights a significant vulnerability in current CAV systems. The study's demonstration of network-wide disruption through V2X communication further emphasizes the potential for widespread chaos. This research underscores the urgent need for robust defense mechanisms against physical adversarial attacks to ensure the safety and reliability of autonomous driving technology. The use of CARLA and SUMO-OMNeT++ for evaluation adds credibility to the findings.
Key Takeaways
- •Anamorphic art can be exploited to create effective adversarial attacks on CAVs.
- •Black-box attacks pose a significant threat due to their lack of reliance on model access.
- •V2X communication can be leveraged to amplify the impact of adversarial attacks.
“PHANTOM achieves over 90\% attack success rate under optimal conditions and maintains 60-80\% effectiveness even in degraded environments.”