Geometric origin of adversarial vulnerability in deep learning
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918135574036480 |
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| author | Ren, Yixiong Du, Wenkang Zhou, Jianhui Huang, Haiping |
| author_facet | Ren, Yixiong Du, Wenkang Zhou, Jianhui Huang, Haiping |
| contents | How to balance training accuracy and adversarial robustness has become a challenge since the birth of deep learning. Here, we introduce a geometry-aware deep learning framework that leverages layer-wise local training to sculpt the internal representations of deep neural networks. This framework promotes intra-class compactness and inter-class separation in feature space, leading to manifold smoothness and adversarial robustness against white or black box attacks. The performance can be explained by an energy model with Hebbian coupling between elements of the hidden representation. Our results thus shed light on the physics of learning in the direction of alignment between biological and artificial intelligence systems. Using the current framework, the deep network can assimilate new information into existing knowledge structures while reducing representation interference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geometric origin of adversarial vulnerability in deep learning Ren, Yixiong Du, Wenkang Zhou, Jianhui Huang, Haiping Machine Learning Statistical Mechanics Neurons and Cognition How to balance training accuracy and adversarial robustness has become a challenge since the birth of deep learning. Here, we introduce a geometry-aware deep learning framework that leverages layer-wise local training to sculpt the internal representations of deep neural networks. This framework promotes intra-class compactness and inter-class separation in feature space, leading to manifold smoothness and adversarial robustness against white or black box attacks. The performance can be explained by an energy model with Hebbian coupling between elements of the hidden representation. Our results thus shed light on the physics of learning in the direction of alignment between biological and artificial intelligence systems. Using the current framework, the deep network can assimilate new information into existing knowledge structures while reducing representation interference. |
| title | Geometric origin of adversarial vulnerability in deep learning |
| topic | Machine Learning Statistical Mechanics Neurons and Cognition |
| url | https://arxiv.org/abs/2509.01235 |