Deep Learning Model Adversarial Training Robustness
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Solution Overview
Problem
Deep learning models face robust overfitting issues during adversarial training, leading to a generalization gap where further training decreases robust test accuracy, despite improving natural test accuracy, due to excessive memorization of one-hot labels, especially for 'hard' samples.
Innovation Solution
Assigning low predictive confidence to adversarially augmented samples with noisy labels and leveraging all training samples to prevent excessive memorization, while fitting 'easy' samples with one-hot labels, and integrating temporal ensembling to regularize predictions and reduce overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training is performed to improve robustness against adversarial examples, then robustness is improved, but robust overfitting occurs leading to a generalization gap where robust test accuracy decreases
Solution Approach 1:
The patent applies local quality by differentiating the treatment of training samples based on their adversarial vulnerability. Easy samples (those that remain correctly classified under adversarial perturbations) receive standard one-hot labels, while hard samples (those that become misclassified) are assigned low predictive confidence labels. This localized differentiation in label quality prevents the model from overfitting to noisy adversarial labels while maintaining robustness.
Solution Approach 2:
The patent changes the parameter of label confidence from a uniform high value to a variable parameter that depends on adversarial stability. By computing the predictive confidence based on whether samples maintain correct classification under threat model perturbations, the label quality parameter dynamically adjusts, reducing overfitting on hard samples while preserving learning signals from easy samples.
2Measurement precision
If further training is continued to decrease robust training loss, then robust training loss decreases, but robust test loss increases indicating overfitting
Solution Approach 1:
The patent implements feedback by using adversarial stability information to modulate label confidence during training. The model's performance under adversarial perturbations feeds back into the labeling process, where samples showing instability (hard samples) receive reduced confidence labels. This feedback loop prevents the model from chasing spurious patterns in adversarial examples, thereby preventing overfitting while continuing to reduce training loss.
3Measurement precision
If standard deep learning training is used to achieve high natural accuracy, then natural accuracy is improved, but the model becomes vulnerable to adversarial examples
Solution Approach 1:
The patent segments the training process into two distinct phases: natural training to achieve high accuracy on clean data, and adversarial training with differentiated label confidence to improve robustness. This segmentation allows the model to first learn correct natural patterns, then selectively adapt to adversarial scenarios only for stable samples, avoiding the trade-off between natural accuracy and adversarial vulnerability.
Data Source
AI summary
A method for deep learning. The method includes: receiving, by a deep learning model, a plurality of samples and a plurality of labels corresponding to the plurality of samples; adversarially augmenting, by the deep learning model, the plurality of samples based on a threat model; and assigning, by the deep learning model, a low predictive confidence to one or more adversarially augmented samples of the plurality of adversarially augmented samples having noisy labels due to the adversarially augmenting based on the threat model.