Understanding adversarial robustness via critical attacking route
| Autor: | Xiaofei Xie, Xianglong Liu, Chongzhi Zhang, Aishan Liu, Tianlin Li, Yitao Xu |
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| Rok vydání: | 2021 |
| Předmět: |
Information Systems and Management
business.industry Computer science Deep learning 05 social sciences 050301 education 02 engineering and technology Computer Science Applications Theoretical Computer Science Adversarial system Artificial Intelligence Control and Systems Engineering Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business 0503 education Software Computer Science::Cryptography and Security |
| Zdroj: | Information Sciences. 547:568-578 |
| ISSN: | 0020-0255 |
| Popis: | Deep neural networks (DNNs) are vulnerable to adversarial examples which are generated by inputs with imperceptible perturbations. Understanding adversarial robustness of DNNs has become an important issue, which would for certain result in better practical deep learning applications. To address this issue, we try to explain adversarial robustness for deep models from a new perspective of critical attacking route, which is computed by a gradient-based influence propagation strategy. Similar to rumor spreading in social networks, we believe that adversarial noises are amplified and propagated through the critical attacking route. By exploiting neurons’ influences layer by layer, we compose the critical attacking route with neurons that make the highest contributions towards model decision. In this paper, we first draw the close connection between adversarial robustness and critical attacking route, as the route makes the most non-trivial contributions to model predictions in the adversarial setting. By constraining the propagation process and node behaviors on this route, we could weaken the noise propagation and improve model robustness. Also, we find that critical attacking neurons are useful to evaluate sample adversarial hardness that images with higher stimulus are easier to be perturbed into adversarial examples. |
| Databáze: | OpenAIRE |
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