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Updated: Jul 6, 2025
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Published on: December 6, 2024
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Adversarially robust neural networks with feature uncertainty learning and label embedding.
Ran Wang1, Haopeng Ke2, Meng Hu3
1School of Mathematical Science, Shenzhen University, Shenzhen, 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, 518060, China; Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, 518060, China.
Summary
This study introduces Margin-SNN, a novel defense method using stochastic neural networks (SNNs) to enhance deep neural network (DNN) adversarial robustness. Margin-SNN improves security by learning feature uncertainty and embedding labels for better class separation, outperforming standard adversarial training.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Computer Vision
Background:
- Deep neural networks (DNNs) face significant security risks due to adversarial examples.
- Existing defense mechanisms often require complex adversarial training, increasing computational cost.
Purpose of the Study:
- To propose a novel defense method, Margin-SNN, to enhance the adversarial robustness of DNNs.
- To improve DNN security without introducing adversarial information during training, offering a more efficient solution.
Main Methods:
- Margin-SNN utilizes stochastic neural networks (SNNs) with two key modules: feature uncertainty learning and label embedding.
- The feature uncertainty module introduces distributional representations in the latent space, promoting intra-class compactness via variational information bottleneck.
- The label embedding module maps labels into the feature space, enhancing inter-class separability by enlarging margins between classes.
Main Results:
- Extensive experiments on MNIST, FASHION MNIST, CIFAR10, CIFAR100, and SVHN datasets demonstrated the superior defensive capabilities of Margin-SNN.
- The proposed method achieves improved adversarial robustness through standard training, proving more efficient than adversarial training.
Conclusions:
- Margin-SNN offers an effective and efficient approach to bolster DNN adversarial robustness.
- The method enhances security by leveraging feature uncertainty and label semantics, paving the way for more secure AI systems.
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