Neural Network Robustness via Constrained Feature Encoding
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Solution Overview
Problem
Current neural networks are vulnerable to adversarial attacks, with existing defense techniques being either computationally expensive or ineffective against specific types of attacks, and there is a need for a robust solution to ensure reliability in classification tasks.
Innovation Solution
The implementation of an additional fully-connected layer with reduced dimensionality and n-hot encoding in neural networks, which constrains the number of features used for classification and enhances robustness against adversarial examples by learning high-information features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training is used to defend neural networks, then robustness against adversarial attacks is improved, but computational cost increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining a constrained feature subset during model training that will be used for making predictions. This constraint is established beforehand, avoiding the need for computationally expensive adversarial training while still providing robustness. The constrained feature subset is identified and fixed before deployment, so no additional computational resources are needed during adversarial defense operations.
Solution Approach 2:
The patent extracts and isolates a specific subset of features that are most important for classification, separating them from the full feature set. By taking out only the necessary features and using exclusively for predictions, the model achieves robustness without processing the entire feature set, thereby reducing computational cost while maintaining reliability.
2Productivity
If randomized smoothing is used to reduce computational overhead, then computational efficiency is improved, but model robustness is weakened
Solution Approach 1:
The patent establishes the constrained feature subset during the training phase, performing the important selection work in advance. This preliminary action allows the model to maintain robustness without needing randomized smoothing during inference, as the constraint is already baked into the model architecture. The efficiency gain is achieved because no additional smoothing operations are needed at prediction time.
Solution Approach 2:
The patent changes the parameter configuration by limiting the number of features available for prediction. Instead of using all features or applying stochastic smoothing, the model operates with a fixed, constrained subset of features. This parameter change (from full feature set to constrained subset) provides both computational efficiency and robustness simultaneously.
3Reliability
If gradient shattering is used to break adversarial attacks, then defense against white box attacks is improved, but adaptability to new attack methods deteriorates
Solution Approach 1:
The patent changes the fundamental parameter of feature selection by using a constrained feature subset instead of relying on gradient shattering techniques. This parameter change makes the model inherently more robust to adversarial attacks without introducing non-differentiable operations that adversaries can adapt to. The approach is versatile because it works with any attack type by simply restricting the feature space.
Solution Approach 2:
The patent extracts and removes vulnerable features from the model, keeping only the most robust and informative features. By taking out the features that are most susceptible to adversarial manipulation and using only the constrained subset, the model achieves defense against various attack types without needing to adapt to specific attack methodologies.
Data Source
AI summary
A neural network is augmented to enhance robustness against adversarial attack. In this approach, a fully-connected additional layer is associated with a last layer of the neural network. The additional layer has a lower dimensionality than at least one or more intermediate layers. After sizing the additional layer appropriately, a vector bit encoding is applied. The encoding comprises an encoding vector for each output class. Preferably, the encoding is an n-hot encoding, wherein n represents a hyperparameter. The resulting neural network is then trained to encourage the network to associated features with each of the hot positions. In this manner, the network learns a reduced feature set representing those features that contain a high amount of information with respect to each output class, and/or to learn constraints between those features and the output classes. The trained neural network is used to perform a classification that is robust against adversarial examples.