Neural Network Boundary Refinement for Certified Adversarial Robustness
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Solution Overview
Problem
Deep neural networks are highly vulnerable to adversarial attacks, and existing defense methods, such as adversarial training, cannot be proven robust against all adversaries, leading to over-fitting and training instability.
Innovation Solution
A method for refining the decision boundary by customizing perturbation magnitude and re-weighting values based on the distance to the decision boundary, using techniques like IBP and CROWN-IBP, to improve certified robustness without additional computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training is used to improve robustness against attacks, then the model's resistance to adversarial examples is improved, but the model suffers from over-fitting and training instability
Solution Approach 1:
The patent applies parameter changes by modifying the perturbation magnitude (epsilon) dynamically during training based on the distance to the decision boundary. Different perturbation levels are used for different data points, allowing the model to learn robust boundaries without uniform over-perturbation that causes instability. This is achieved through the re-weighting mechanism that adjusts epsilon per sample based on its proximity to the decision boundary.
Solution Approach 2:
The patent implements local quality by applying different perturbation magnitudes to different regions of the input space. Specifically, data points closer to the decision boundary receive larger perturbation magnitudes, while points farther away receive smaller magnitudes. This localized approach allows robust training where it is most needed (near boundaries) without introducing unnecessary noise elsewhere, thus improving stability.
2Reliability
If larger perturbation magnitude is used to strengthen adversarial training, then the model's robustness is improved, but the training loss increases and convergence becomes difficult
Solution Approach 1:
The patent dynamically changes the perturbation magnitude parameter based on the decision boundary distance. By computing the distance from each data point to the decision boundary and using this distance to scale the perturbation magnitude, the method ensures that perturbation strength is adaptive rather than fixed. This allows achieving robustness certification with moderate overall loss by concentrating perturbation where it matters most.
Solution Approach 2:
The patent applies partial action by using larger perturbation magnitudes only for data points that are close to the decision boundary, rather than applying uniform large perturbations to all data points. This selective approach achieves the necessary robustness for certification while minimizing the overall training loss impact, as most data points may not require strong perturbation.
3Ease of operation
If uniform perturbation is applied to all data points, then the training process is simple, but the model cannot achieve optimal robustness for data points at different distances from the decision boundary
Solution Approach 1:
The patent implements local quality by computing the distance from each data point to the decision boundary and using this distance to determine the appropriate perturbation magnitude. This creates a localized training approach where each data point receives perturbation tailored to its specific position in the feature space, achieving optimal robustness precision while maintaining reasonable operational complexity through automated distance-based scaling.
Solution Approach 2:
The training process performs self-service by automatically adjusting perturbation magnitudes based on intrinsic properties of the data (distance to decision boundary). The system computes distances and scales perturbations without requiring manual intervention or complex hyperparameter tuning for each data point, thus maintaining ease of operation while achieving precision.
Data Source
AI summary
A computer implemented method for certifying robustness of image classification in a neural network is provided. The method includes initializing a neural network model. The neural network model includes a problem space and a decision boundary. A processor receives a data set of images, image labels, and a perturbation schedule. Images are drawn from the data set in the problem space. A distance from the decision boundary is determined for the images in the problem space. A re-weighting value is applied to the images. A modified perturbation magnitude is applied to the images. A total loss function for the images in the problem space is determined using the re-weighting value. A confidence level of the classification of the images in the data set is evaluated for certifiable robustness.