Driver Assistance Perception Screening via Frequency-Domain Attack Detection
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Solution Overview
Problem
Modern driver assistance systems in autonomous vehicles are susceptible to adversarial attacks, which can lead to significant drops in accuracy and pose safety risks due to the malfunction of neural networks used for perception, and existing solutions either require excessive computational power or are ineffective against strong attacks.
Innovation Solution
A method involving frequency analysis, such as discrete Fourier transformation, is applied to detect adversarial attacks by analyzing the frequency spectrum of sensor data before processing, using techniques like denoising filters and neural networks trained in the frequency domain to identify and mitigate adversarial noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardening neural networks against adversarial attacks is implemented, then reliability is improved, but device complexity and computational power requirements worsen
Solution Approach 1:
The system segments the neural network processing into two distinct parts: a lightweight frequency analysis module that detects adversarial attacks, and the main neural network that processes clean data. This segmentation allows the detection function to be separated from the complex neural network, reducing overall computational complexity while maintaining reliability.
Solution Approach 2:
A frequency analysis intermediary is introduced between the sensor input and the neural network. This intermediary transforms sensor data into the frequency domain, detects adversarial patterns, and either blocks or cleans the data before it reaches the neural network. This mediator handles the adversarial detection task efficiently without requiring the neural network itself to be hardened.
2Difficulty of detecting and measuring
If feature squeezing techniques are used to detect adversarial attacks, then detection capability is improved, but effectiveness against strong attacks worsens
Solution Approach 1:
The system changes the parameter domain from spatial to frequency domain for adversarial detection. By transforming sensor data into the frequency domain and analyzing spectral characteristics, the system can detect subtle adversarial patterns that feature squeezing methods miss, especially under strong attacks. This parameter transformation enables more robust detection across varying attack intensities.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively detects multiple types of adversarial attacks with varying strengths, ensuring reliable operation of driver assistance systems with minimal computational overhead, thereby enhancing safety by preventing malfunction and enabling real-time applications.
Implementation Method 1
A method involving frequency analysis, such as discrete Fourier transformation, is applied to detect adversarial attacks by analyzing the frequency spectrum of sensor data
Implementation Method 2
using techniques like denoising filters and neural networks trained in the frequency domain to identify and mitigate adversarial noise
Data Source
AI summary
The disclosure relates to a method for controlling a driver assistance system during operation of a, especially partially automated, fully automated or autonomous, vehicle, wherein the driver assistance system comprises: a, for example at least one, sensor for observing an environment of the vehicle and an electronic control unit using a, especially at least one, neural network for analyzing sensor data of the sensor and providing perception tasks based on the analyzed sensor data, the method comprising:providing a data set of the sensor data by the sensor) in a spatial domain;transforming the data set of the sensor data by the electronic control unit using frequency analysis into a frequency spectrum in a frequency domain; andanalyzing the frequency spectrum of the data set in order to detect an adversarially attacked data set, for example before analyzing the sensor data for providing perception tasks.