Wiener Image Filtering for Adversarially Robust Neural Vision
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models, particularly deep neural networks, are susceptible to adversarial perturbations, leading to non-transparency, vulnerability to adversarial attacks, and difficulty in domain adaptation, which affects their robustness and reliability in applications like automated driving.
Innovation Solution
Applying a Wiener filter in the frequency domain to suppress adversarial perturbations by recognizing and filtering out specific frequency patterns associated with adversarial content, using a method that includes transforming image data into the frequency domain, applying a filter to suppress these patterns, and then transforming back to the spatial domain for processing by the machine-learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used for image processing, then processing capability and accuracy are improved, but susceptibility to adversarial perturbations increases
Solution Approach 1:
A frequency domain filter is introduced as an intermediary component between the input image and the deep neural network. The filter transforms the image into the frequency domain, suppresses adversarial perturbations, and transforms back to spatial domain before network processing, thereby protecting the network while maintaining its processing capabilities
Solution Approach 2:
The invention changes the representation parameters of the image data by transforming from spatial domain to frequency domain. This parameter transformation allows selective suppression of adversarial frequencies while preserving legitimate image information, resolving the contradiction between accuracy and robustness
2Reliability
If frequency domain filtering is applied to suppress adversarial perturbations, then robustness is improved, but computational complexity increases
Solution Approach 1:
The filter applies partial action by selectively suppressing only the frequency components corresponding to adversarial perturbations rather than processing the entire image uniformly. This targeted approach reduces computational overhead while maintaining robustness effectiveness
3Reliability
If adversarial training is performed to improve robustness, then reliability is improved, but training time and computational resources increase
Solution Approach 1:
The frequency domain filter serves as a preliminary action that pre-processes input images to remove adversarial perturbations before they reach the neural network. This prevents the network from learning vulnerable patterns during training, reducing the need for extensive adversarial training while maintaining robustness
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
Enhances the robustness of neural networks against adversarial attacks with minimal computational overhead, maintaining performance on clean images and improving the reliability of machine-learning models in applications such as object detection and image segmentation.
Implementation Method 1
transforming the image data into a frequency domain, applying a filter, in particular a Wiener filter, on the image data in the frequency domain
Data Source
AI summary
A method, system and computer program for processing image data, to a vehicle comprising such a system, and to a method, system and computer program for generating a filter. Image data processing may include obtaining the image data, and applying a filter on the image data to generate filtered image data, the filter being configured to suppress adversarial perturbations within the image data. The filtered image data is processed using a machine-learning model.