arXiv:2607.05649v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) face increasing threats from vandalism-induced occlusion attacks (VOAs) that compromise camera-based perception. While detection frameworks can identify vandalized images, restoring camera-stream utility after physical occlusion remains underexplored. This paper presents present the Recovery and Enhancement of Vandalized Images for Vision Excellence (REVIVE) framework, a vandalism recovery pipeline integrating: (1) binary VOA detection, (2) multi-class VOA pattern identification, (3) EfficientNet-based U-Net segmentati
The proliferation of autonomous vehicles (AVs) and their reliance on camera-based perception makes them increasingly vulnerable to physical attacks, necessitating robust defence mechanisms.
This research addresses a critical vulnerability in AV security and reliability, directly impacting their commercial viability and public acceptance.
The ability to detect and recover from vandalism in AVs will improve their operational resilience and accelerate their deployment in complex urban environments.
- · Autonomous Vehicle Manufacturers
- · AI/Computer Vision Developers
- · Smart City Infrastructure
- · Criminal Actors
- · Vulnerable Transportation Systems
Improved safety and reliability of autonomous vehicle operations.
Accelerated adoption and commercialization of self-driving technology due to enhanced security.
New standards and regulations for AV physical security and perception system resilience could emerge globally.
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