Adversarial Robustness by One Bit Double Quantization for Visual Classification
In this paper, we propose a novel robust visual classification framework that uses double quantization (dquant) to defend against adversarial examples in a specific attack scenario called “subsequent adversarial examples” where test images are injected with adversarial noise. T...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8928531/ |