Similarity search can help detect spoofing attacks on self-driving sensors by identifying anomalies in sensor data through comparison with known valid patterns. Spoofing attacks involve feeding fake signals—like forged LiDAR point clouds, manipulated camera images, or counterfeit radar returns—to trick the vehicle’s perception system. By using similarity search, developers can compare incoming sensor data against a preprocessed dataset of verified, real-world examples. If the new data deviates significantly from expected patterns, the system flags it as suspicious. This approach works because spoofed data often lacks the natural noise, spatial relationships, or temporal consistency of authentic sensor outputs, making it stand out when analyzed at scale.
For example, consider a camera-based spoofing attack where an adversary projects a fake image of a stop sign onto a roadside object. A similarity search system could compare the incoming image frame against a database of genuine stop sign images captured in varying lighting, angles, and weather conditions. Using techniques like feature extraction (e.g., edge detection, color histograms) or embedding vectors from neural networks, the system computes how closely the new image matches the known examples. If the similarity score falls below a threshold—say, because the forged sign lacks realistic wear-and-tear or perspective distortion—the system raises an alert. Similarly, for LiDAR, a spoofed point cloud might have unnaturally uniform density or miss subtle environmental details like foliage movement, which a similarity search could detect by comparing it to historical LiDAR scans of the same area.
Implementing this requires building a reference dataset of normal sensor data and selecting efficient similarity metrics. Tools like k-nearest neighbors (k-NN) algorithms, approximate nearest neighbor libraries (e.g., FAISS), or autoencoders can be used to compute similarities in real time. However, developers must balance accuracy and latency—self-driving systems need results within milliseconds. One practical approach is to precompute embeddings for common scenarios (e.g., urban intersections, highway driving) and use lightweight models to compare live data against these clusters. Challenges include handling sensor-specific noise (e.g., radar multipath effects) and avoiding false positives caused by rare but legitimate scenarios. By integrating similarity search with other defenses—like cross-sensor validation or cryptographic sensor authentication—developers can create layered protection against spoofing while maintaining system performance.