Frequency-Domain Image Filtering Against Adversarial Perturbations
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Solution Overview
Problem
Deep neural networks are susceptible to adversarial perturbations, which are imperceptible manipulations that can significantly affect their output, and existing defense mechanisms are computationally complex, do not scale well, and do not improve comprehensibility, posing challenges for robustness and efficient deployment in real-world applications.
Innovation Solution
Applying a Wiener filter in the frequency domain to suppress adversarial perturbations by recognizing their characteristic patterns, which are visible in the frequency domain, thereby enhancing the robustness of neural networks without significant computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If deep neural networks are trained using purely data-driven parameter fitting without expert intervention, then the training process is simplified and automation is improved, but the network becomes susceptible to adversarial perturbations and loses robustness
Solution Approach 1:
The patent applies preliminary action by introducing a denoising autoencoder before the main neural network processing. This autoencoder is trained in advance to recognize and remove adversarial perturbations from input images before they reach the classification network, thereby preventing the network from being fooled by adversarial examples while maintaining the data-driven training approach
Solution Approach 2:
The denoising autoencoder serves as an intermediary component between the input image and the classification network. It acts as a mediator that cleans the input data by removing adversarial perturbations, allowing the main network to process only clean, legitimate image data without being exposed to harmful adversarial examples
2Reliability
If adversarial training is performed to improve robustness, then reliability against adversarial attacks is improved, but device complexity and computational overhead increase
Solution Approach 1:
The patent segments the robustness problem into two independent components: a denoising autoencoder for removing perturbations and a main classification network for processing clean data. This segmentation allows each component to be optimized separately, reducing the overall complexity compared to redesigning the entire network for adversarial robustness
3Reliability
If post-processing methods are applied to ensure stability and robustness, then reliability is improved, but processing time and productivity are reduced
Solution Approach 1:
The denoising autoencoder is integrated as a continuous preprocessing step that operates in real-time alongside the main network inference. This continuous action ensures that robustness is maintained without requiring separate post-processing steps, thereby minimizing additional processing time and maintaining high productivity
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
The proposed method effectively suppresses adversarial perturbations across various attacks, maintaining performance on clean images and reducing latency, thus improving the robustness and efficiency of neural networks in real-world applications.
Implementation Method 1
The filter is configured to suppress adversarial perturbations within the image data by identifying and filtering out specific frequency patterns in the frequency domain
Implementation Method 2
applying a filter on the image data to generate filtered image data... The filter is configured to suppress adversarial perturbations within the image data
Data Source
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Figure 1c
Figure 2a~2b
AI summary
Various aspects of the present disclosure relate to 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. The method for processing image data comprises obtaining (110) the image data. The method comprises applying (120) a filter on the image data to generate filtered image data, the filter being configured to suppress adversarial perturbations within the image data. The method comprises processing (130) the filtered image data using a machine-learning model.