Autonomous Vehicle Security

Advanced

Autonomous vehicles add new attack surfaces including sensor systems, ML models, and V2X communications. Adversarial attacks can cause dangerous misclassifications.

Sensor Attack Surfaces

LiDAR Attacks

• Spoofing fake objects
• Blinding with lasers
• Relay attacks
• Point cloud manipulation

Camera Attacks

• Adversarial patches
• Traffic sign modification
• Projected patterns
• Blinding/saturation

Radar Attacks

• Ghost vehicle injection
• Distance spoofing
• Jamming
• Replay attacks

GPS Spoofing

• Location manipulation
• Route deviation
• Time attacks
• Map confusion

Adversarial ML Examples

adversarial-patch.py

python

# Adversarial patch generation (conceptual)
# Stop sign misclassified as speed limit

import torch
from torchvision import models

# Load target model (e.g., traffic sign classifier)
model = models.resnet50(pretrained=True)

# Generate adversarial patch
# Optimize patch to cause misclassification
# Physical-world constraints:
# - Print resolution
# - Viewing angle invariance
# - Lighting conditions

# Real-world examples:
# - Stickers on stop signs
# - Patterns on road surface
# - Modified lane markings
# - Projected images at night

Safety Critical

Autonomous vehicle attacks can have life-threatening consequences. Research should only be conducted in controlled environments with proper safety measures.