Composite Adversarial Training for Neural Network Robustness
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Solution Overview
Problem
State-of-the-art neural networks are vulnerable to adversarial attacks, where malicious data points can mislead the models, affecting applications such as biometric authentication and autonomous driving, and existing methods fail to effectively defend against these attacks.
Innovation Solution
A computer system and method that uses a composite adversarial attack process to train neural networks by applying a series of adversarial perturbations with specific attack types, power values, and intervals, optimized through iterative gradient descent, to generate robust models capable of identifying and resisting adversarial perturbations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial perturbations are introduced to data samples, then the model's performance on benign data points is maintained, but the model becomes vulnerable to misclassification attacks
Solution Approach 1:
The patent applies preliminary anti-action by performing adversarial training before deployment, where the model is pre-exposed to adversarial perturbations during training to learn defensive patterns. The training process generates adversarial examples and uses them to update model parameters, creating preliminary immunity against future attacks while maintaining benign data performance.
Solution Approach 2:
The patent converts harmful adversarial perturbations into beneficial training signals. Adversarial examples that would normally cause misclassification are used as training data with gradient-based optimization, transforming the harmful attack mechanism into a useful defense mechanism that improves model robustness against such attacks.
2Adaptability or versatility
If multiple adversarial attack types are applied to data samples, then the coverage of attack scenarios is improved, but the complexity of the training process increases
Solution Approach 1:
The patent merges multiple adversarial attack types into a unified composite training framework. Different attack methods (e.g., FGSM, PGD, AutoAttack) are combined and applied sequentially or in parallel during training, allowing the model to learn against diverse attack patterns simultaneously while managing complexity through structured integration.
Solution Approach 2:
The patent creates a universal adversarial training system that can handle multiple attack types through a common framework. The training pipeline is designed to accommodate various perturbation methods and attack scenarios using standardized interfaces and configurable parameters, enabling multi-functionality without proportionally increasing complexity.
3Reliability
If composite adversarial attacks are performed during training, then the model's robustness against malicious data points is enhanced, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary adversarial attacks during the training phase to preemptively strengthen the model's defenses. By conducting composite adversarial attacks and gradient updates before deployment, the model acquires robustness in advance, reducing the need for prolonged fine-tuning or retraining after deployment and thus managing overall time investment.
Data Source
AI summary
Some embodiments of the present disclosure are directed to systems, computer-readable media, and computer-implemented methods for neural network training. Some embodiments are directed to determining an attack order schedule for the data sample that includes a plurality of adversarial perturbation attacks associated with the data sample, and performing a composite adversarial attack process against the data set using the determined attack order schedule to generate a perturbed data sample for the data sample. Other embodiments may be disclosed or claimed.