ML Security Layer for Camera-LiDAR Fusion Against Adversarial Noise
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Solution Overview
Problem
Deep learning models used in autonomous vehicles are vulnerable to adversarial attacks, which can lead to safety-critical errors, such as misinterpreting traffic lights, due to their complexity and multiple attack pathways.
Innovation Solution
Implementing a security technique that uses a second machine learning model to detect artificial data noise associated with adversarial attacks by training it to minimize a population risk function, and further enhancing security through camera-LiDAR fusion systems that separate data into patches and use cross-attention processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for object detection and classification in autonomous vehicles, then detection accuracy and classification performance are improved, but vulnerability to adversarial attacks increases
Solution Approach 1:
The patent introduces an intermediary security layer between the sensor data and the deep learning model. This intermediary consists of multiple security techniques including adversarial training, input validation, and anomaly detection mechanisms that filter and verify data before it reaches the main detection model, thereby protecting against adversarial attacks while preserving detection accuracy
Solution Approach 2:
The patent applies preliminary security measures by pre-training models with adversarial examples, establishing validation rules beforehand, and preparing defense mechanisms before actual deployment. This preliminary action ensures the system is already protected when exposed to adversarial attacks during operation
2Measurement precision
If multimodal sensor fusion systems are implemented, then object detection performance is improved, but complexity and vulnerability to adversarial attacks increase
Solution Approach 1:
The patent segments the complex multimodal fusion system into separate processing streams for different sensor types (camera, LiDAR, radar). Each modality is processed independently through dedicated neural network branches, which are then combined at a later stage. This segmentation reduces overall model complexity while maintaining the benefits of multimodal fusion
3Loss of information
If more sensor data is collected and processed, then situational awareness is improved, but exposure to adversarial attacks increases
Solution Approach 1:
The patent introduces validation intermediaries that check each sensor data stream for adversarial indicators before allowing it to contribute to the overall situational awareness. These intermediaries act as gatekeepers that prevent compromised data from affecting the system while preserving legitimate sensor information
Data Source
AI summary
This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving, by a processor, image data from a camera image sensor; receiving, by the processor, point cloud data from a light detection and ranging (LiDAR) sensor; generating, by the processor and using a first machine learning model, fused image data that combines the image data and the point cloud data; and determining, by the processor and using a second machine learning model, whether the fused image data satisfies a criteria based on whether a population risk function of the first machine learning model exceeds a threshold. Other aspects and features are also claimed and described.