Neural Network Robustness via Adversarial Data Manipulation
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Solution Overview
Problem
Current neural networks, particularly convolutional neural networks, are insufficiently robust against interference and adversarial attacks, leading to misclassification or incorrect semantic segmentation despite unchanged sensor data.
Innovation Solution
A method that manipulates the training data set to maintain semantically meaningful content while altering insignificant aspects, allowing for parameter and architecture changes in the neural network based on comparisons between original and altered data sets, thereby enhancing robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained on original training data to achieve high functional accuracy, then classification performance is improved, but robustness against interference and adversarial attacks deteriorates
Solution Approach 1:
The method applies preliminary actions by generating manipulated training data sets before the main training process. These manipulated data sets, which include adversarial examples and interference patterns, are prepared in advance to strengthen the neural network's robustness during subsequent training, preventing vulnerability to attacks rather than fixing it afterward
Solution Approach 2:
The method converts harmful interference patterns and adversarial attacks into beneficial training signals. By intentionally introducing manipulated data with various interference types into the training process, the neural network learns to recognize and resist these attacks, transforming potential threats into opportunities for improving robustness
2Reliability
If neural networks are made more robust through traditional methods, then reliability is improved, but functional accuracy and performance deteriorate
Solution Approach 1:
The method applies local quality by selectively manipulating specific regions and features of training data rather than uniformly altering all inputs. By targeting specific neurons, filters, and data regions with localized transformations, the method strengthens robustness in critical areas while preserving functional accuracy in other regions
Solution Approach 2:
The method employs parameter changes by systematically varying training data parameters such as pixel values, feature magnitudes, and input distributions. These controlled parameter transformations allow the neural network to learn invariant representations that maintain accuracy while gaining robustness against parameter perturbations
3Reliability
If training data is manipulated to enhance robustness, then reliability is improved, but training complexity and computational resources increase
Solution Approach 1:
The method applies segmentation by dividing the training process into distinct phases: generating manipulated data sets, training on original data, training on manipulated data, and evaluating robustness. This segmented approach manages complexity by breaking down the challenging task of robust training into manageable, sequential steps with clear objectives for each phase
Solution Approach 2:
The method introduces an intermediary component that generates manipulated training data sets based on the original training data. This intermediary layer transforms raw training data into enhanced robustness training examples, mediating between the original data and the neural network to reduce the complexity burden on the main training process
Data Source
AI summary
The invention relates to a method for making a neural network more robust in a function-specific manner, comprising the following steps: providing the neural network, wherein the neural network is/has been trained on the basis of a training data set including training data; generating at least one changed training data set by manipulating the training data set, wherein the training data is changed while maintaining semantically meaningful content; changing parameters and/or an architecture of the neural network according to a comparison result of a comparison between an application of the original training data set and the at least one changed training data set on the trained neural network; training the changed neural network on the basis of the training data set and at least one part of the at least one changed training data set. The invention also relates to a device, to a computer program product, and to a computer-readable storage medium.