Tracker-Based Vision Attack Detection for Autonomous Vehicles
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles are vulnerable to malicious attacks on their camera systems, which can lead to false object detection and navigation errors, as existing methods lack comprehensive solutions to detect and mitigate such vision attacks.
Innovation Solution
Implementing a multi-faceted detection system that performs temporal consistency checks, inconsistency counter checks, and past history checks on camera images to identify inconsistencies, and taking mitigation actions such as removing malicious tracks or reporting attacks to external authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple detection processes are implemented to detect image inconsistencies, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The detection system is divided into multiple independent detection processes (temporal consistency check, inconsistency counter check, past history check), each responsible for detecting specific types of image inconsistencies. This segmentation allows the system to achieve comprehensive detection reliability while maintaining modular complexity that can be managed and optimized independently.
Solution Approach 2:
The detection system performs multiple functions through a unified multi-faceted detection framework that handles temporal consistency, inconsistency counting, and historical comparison simultaneously. This multi-functionality approach improves detection reliability across different attack scenarios while avoiding the need for separate dedicated systems for each detection type.
2Measurement precision
If comprehensive detection processes are performed on all images, then detection precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary filtering by comparing current images against previously processed images and maintaining inconsistency counters. This preliminary action allows the system to quickly identify potential attacks before applying more comprehensive analysis, thereby improving detection precision while reducing overall processing time through early detection and filtering.
Solution Approach 2:
The detection system applies different levels of analysis based on the situation - using lightweight temporal consistency checks for routine monitoring and escalating to more comprehensive past history checks and inconsistency counter analysis only when anomalies are detected. This partial action approach maintains high detection precision while minimizing processing time during normal operation.
Data Source
AI summary
Various embodiments include methods for identifying inconsistencies in images that could be due to malicious attacks. Various embodiments may include receiving a plurality of camera images from one or more cameras of an apparatus (e.g., a vehicle), performing a plurality of different processes on the plurality of images to detect different types of image inconsistencies, using results of the plurality of different processes on the plurality of images to recognize a vision attack and performing one or more mitigation actions in response to recognizing a vision attack. The plurality of different processes may include temporal consistency checks on the plurality of images spanning a period of time, inconsistency counter checks on the plurality of images that determine whether a number of inconsistencies in camera images satisfies a threshold, past history checks on the plurality of images comparing objects previously recognized to objects recognized in currently obtained images.