REVIVE: A Multi-Modal Framework for Vandalism Detection and Recovery in Autonomous Vehicles

Researchers introduced REVIVE, a multi-modal framework for detecting and recovering from vandalism-induced occlusion attacks on autonomous vehicle cameras. The system combines detection, segmentation, and type-aware inpainting to restore perception, achieving near-perfect object-detection recall with direct pixel replacement. This addresses a critical gap in AV security by enabling recovery from physical camera occlusions.

arXiv:2607.05649v1 Announce Type: new 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 segmentation, and 4 type-aware recovery using Bootstrapping Language-Image Pre-training BLIP -guided Stable Diffusion inpainting, direct pixel replacement, or adaptive median filtering. Stable Diffusion shows variable reconstruction performance per-pattern SSIM 0.667-0.867, PSNR 15.4-26.7dB across VOA patterns, while aligned direct pixel replacement achieves near-identical reconstruction under the aligned-reference condition. On 500 tracked clean/vandalized image pairs, unrecovered VOAs reduce YOLOv8l object-detection recall to 0.588, while direct pixel replacement restores recall to 0.967 and F1-score to 0.970 under that aligned-reference condition. LaMa, Telea, and Navier-Stokes baselines improve image similarity but provide more limited downstream detection recovery, and Stable Diffusion is treated as an asynchronous recovery branch subject to a quality gate rather than a blocking real-time perception step. We evaluate a reference-available quality gate that filters recovered candidates before downstream use: without it, type-aware routing degrades per-image recall to 0.304, whereas with it, recall returns to 0.608, at or above the unrecovered baseline, ensuring the forwarded stream is never worse than the unrecovered frame. REVIVE therefore, provides a structured recovery framework from VOAs in AVs.