ADAS Image Detection of Phantom Projection Attacks
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Solution Overview
Problem
Advanced driver assistance systems (ADASs) are vulnerable to split-second phantom attacks, where attackers project images for a brief instant, fooling AI models and causing vehicles to stop or issue false notifications, as these systems rely heavily on image input and struggle to differentiate between real and projected objects in real-time.
Innovation Solution
A system and method that processes images from ADAS sensors to identify traffic objects, generates multiple processed images highlighting different aspects, applies these images to neural network aspect models trained to detect phantom projections, and uses a combiner model to determine if an object is a phantom projection, issuing an indicator for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system uses multiple neural network aspect models and processed images to detect phantom projections, then the reliability of detecting phantom projections is improved, but the device complexity increases
Solution Approach 1:
The detection system is divided into multiple specialized neural network aspect models (e.g., context model, surface model, light model, depth model), each analyzing specific features of detected objects. This segmentation allows each model to focus on particular aspects that indicate phantom projections, improving overall detection reliability while maintaining manageable complexity through modular design
Solution Approach 2:
The system creates multiple processed images from a single captured image, each highlighting different aspects (context, surface, light, depth). This transforms the problem from analyzing one complex image to analyzing multiple simplified representations, enabling more reliable phantom projection detection through multi-dimensional analysis
2Productivity
If the system processes images in real-time to detect phantom projections, then the productivity of vehicle response is improved, but the device complexity increases
Solution Approach 1:
The system pre-processes the captured image to generate multiple aspect-highlighting processed images before applying them to neural network models. This preliminary action organizes the data in advance, enabling faster real-time detection by the neural networks and improving overall vehicle response speed
Solution Approach 2:
By dividing the image processing into separate aspect-specific models (context, surface, light, depth), the system can process different features in parallel, improving computational efficiency and enabling real-time detection despite the increased processing requirements
Data Source
AI summary
A system and methods are provided for determining a vehicle action during a phantom projection attack, including processing a received image to identify a traffic object, and creating from the received image multiple processed images that are applied to respective neural network (NN) models. Latent representations of the multiple processed images from each of the NN models are then fed to a combiner model trained to determine whether the latent representations indicate a phantom projection attack, and, responsively to a determination of a phantom projection attack, issuing a phantom projection indicator.