LAD generates diverse adversarial examples in latent space by perturbing along normals to an SVM-defined decision boundary and uses them for adversarial training to improve DNN robustness.
Extending Defensive Distillation
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abstract
Machine learning is vulnerable to adversarial examples: inputs carefully modified to force misclassification. Designing defenses against such inputs remains largely an open problem. In this work, we revisit defensive distillation---which is one of the mechanisms proposed to mitigate adversarial examples---to address its limitations. We view our results not only as an effective way of addressing some of the recently discovered attacks but also as reinforcing the importance of improved training techniques.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Latent Adversarial Defence with Boundary-guided Generation
LAD generates diverse adversarial examples in latent space by perturbing along normals to an SVM-defined decision boundary and uses them for adversarial training to improve DNN robustness.