Autonomous Vehicle Object Detection Output Authentication
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Solution Overview
Problem
Unauthorized modifications to object detection systems in autonomous vehicles can compromise the accuracy and safety of vehicle operations, leading to decreased customer satisfaction.
Innovation Solution
Implementing a cryptographic signature mechanism to verify the outputs of a neural network's fully connected layer, using a private key stored in a reply-protected memory block, to authenticate and adjust vehicle operations based on verified object detection results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic signature verification is implemented, then system reliability is improved, but device complexity increases
Solution Approach 1:
A cryptographic signature verification module is introduced as an intermediary component between the neural network output and the vehicle control system. This mediator verifies the authenticity of object detection results before they are used to adjust vehicle operation, preventing unauthorized modifications while maintaining system functionality.
Solution Approach 2:
Cryptographic signatures are generated in advance for neural network outputs before they are used for vehicle control decisions. The verification process occurs preliminarily, ensuring that only authenticated outputs are accepted, thereby preventing tampering before it can affect system reliability.
2Reliability
If cryptographic signatures are generated and verified, then unauthorized modification is reduced, but processing time increases
Solution Approach 1:
Cryptographic signatures are applied selectively to critical output nodes of the neural network rather than all outputs. The verification process focuses on specific signature verification points where unauthorized modification is most likely to occur, optimizing the balance between security coverage and processing efficiency.
Data Source
AI summary
Embodiments are disclosed for reducing unauthorized modifications of object detection systems of motor vehicles. As an example, a method comprises: generating a cryptographic signature for an output of nodes of a fully connected layer of a neural network of an object detection system of a vehicle, the cryptographic signature based in part on a first private key stored in a reply-protected memory block (RPMB), the output at least partially describing a detected object; and, responsive to verifying the cryptographic signature, adjusting vehicle operation based on the detected object. In this way, outputs of the neural network may be verified, so that unauthorized modifications are detected, and system accuracy increases.