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

2025-10-20 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

The REVIVE (Recovery and Enhancement of Vandalized Images for Vision Excellence) framework addresses vandalism-induced occlusion attacks (VOAs) on autonomous vehicle camera systems. This multi-modal pipeline integrates binary VOA detection, multi-class VOA pattern identification (five categories), EfficientNet-based U-Net segmentation, and type-aware image recovery. Recovery methods include BLIP-guided Stable Diffusion inpainting, direct pixel replacement, and adaptive median filtering. On 500 tracked clean/vandalized image pairs, unrecovered VOAs reduced YOLOv8l object-detection recall to 0.588. Direct pixel replacement, an upper-bound method requiring an aligned clean reference, restored recall to 0.967 and F1-score to 0.970. A quality gate ensures forwarded frames are never worse than unrecovered, improving recall from 0.304 (without gate) to 0.608 (with gate). REVIVE's preprocessing adds 22.7 ms per frame, fitting a 30 FPS budget.

Key takeaway

For Computer Vision Engineers designing AV perception systems, this research highlights the necessity of integrating robust vandalism recovery. You should prioritize type-aware recovery strategies and implement a quality gate to prevent degraded or hallucinated frames from impacting downstream object detection. Consider direct pixel replacement for short-horizon, aligned reference scenarios, and relegate computationally intensive generative methods like Stable Diffusion to asynchronous processing to maintain real-time performance.

Key insights

Autonomous vehicle camera perception can be robustly restored from vandalism using a type-aware, quality-gated recovery pipeline.

Principles

• Recovery must be type-aware.
• Generative models need quality gates.
• Downstream detection is key metric.

Method

REVIVE employs binary VOA detection, multi-class pattern identification, EfficientNet-U-Net segmentation, and type-aware recovery (median filtering, BLIP-guided Stable Diffusion, or direct pixel replacement), followed by a quality gate.

In practice

• Use direct pixel replacement with aligned references.
• Route complex VOAs to asynchronous generative recovery.
• Implement quality gates for all recovered frames.

Topics

• Autonomous Vehicles
• Vandalism Detection
• Image Recovery
• Object Detection (YOLOv8)
• Stable Diffusion
• Perception Systems

Code references

• ultralytics/ultralytics

Best for: AI Scientist, Research Scientist, Computer Vision Engineer, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.