Deviated Intermediate Representations for Secure V2V Model Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicle technologies face challenges in effectively communicating and aggregating intermediate representations of environments between vehicles, which can lead to inaccuracies in object detection and prediction, and are vulnerable to adversarial attacks.
Innovation Solution
A computer-implemented method for vehicle-to-vehicle communications that involves obtaining sensor data, generating intermediate representations, determining deviations based on machine-learned models, and communicating modified representations to improve autonomous operations and defend against adversarial attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If intermediate representations are communicated between autonomous vehicles, then the accuracy of object detection and prediction is improved through aggregation, but the system becomes vulnerable to adversarial attacks
Solution Approach 1:
The system performs preliminary actions by training the machine-learned model with deviated intermediate representations before deployment. This advance preparation enables the model to recognize and resist adversarial attacks when intermediate representations are communicated between vehicles, thus improving detection accuracy while maintaining security.
Solution Approach 2:
The system applies preliminary anti-action by introducing deviated intermediate representations during training to counteract potential adversarial attacks. This creates a form of immunization where the model learns to identify and reject malicious inputs, thereby protecting the accuracy of object detection and prediction when aggregating data from multiple vehicles.
2Measurement precision
If intermediate representations are aggregated from multiple vehicles, then detection accuracy is enhanced, but the complexity of inter-system communication increases
Solution Approach 1:
The system extracts only the essential intermediate representations needed for object detection and prediction from multiple vehicles, rather than transmitting all sensor data. This selective extraction enhances detection accuracy through aggregation while reducing communication complexity by transmitting only relevant features.
3Reliability
If machine-learned models are trained with deviated intermediate representations, then security against adversarial attacks is improved, but the training process becomes more complex
Solution Approach 1:
The system converts the potential harm of adversarial attacks into a training benefit by deliberately introducing deviated intermediate representations during the training process. This transforms security threats into useful training data that strengthens the model's ability to detect and resist attacks, improving reliability while managing training complexity through purposeful distortion.
Data Source
AI summary
Systems and methods for vehicle-to-vehicle communications are provided. An adverse system can obtain sensor data representative of an environment proximate to a targeted system. The adverse system can generate an intermediate representation of the environment and a representation deviation for the intermediate representation. The representation deviation can be designed to disrupt a machine-learned model associated with the target system. The adverse system can communicate the intermediate representation modified by the representation deviation to the target system. The target system can train the machine-learned model associated with the target system to detect the modified intermediate representation. Detected modified intermediate representations can be discarded before disrupting the machine-learned model.