Practical Black-Box Attacks against Machine Learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow, Somesh Jha, Ananthram Swami
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Citation edge evidence
- Advances and Open Problems in Federated Learning→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
- Boosting Adversarial Attacks with Momentum→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
- Explainable AI: A Review of Machine Learning Interpretability Methods→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
- Robust Physical-World Attacks on Deep Learning Visual Classification→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
- ZOO→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
- One Pixel Attack for Fooling Deep Neural Networks→Practical Black-Box Attacks against Machine LearningLegacy edge — source detail unavailable
- Practical Black-Box Attacks against Machine Learning→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- Practical Black-Box Attacks against Machine Learning→Random sampling with a reservoirLegacy edge — source detail unavailable
- Practical Black-Box Attacks against Machine Learning→Accessorize to a CrimeLegacy edge — source detail unavailable
- Accessorize to a Crime→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- ZOO→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- One Pixel Attack for Fooling Deep Neural Networks→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- Robust Physical-World Attacks on Deep Learning Visual Classification→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- Robust Physical-World Attacks on Deep Learning Visual Classification→Accessorize to a CrimeLegacy edge — source detail unavailable
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Accessorize to a CrimeLegacy edge — source detail unavailable
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→ZOOLegacy edge — source detail unavailable
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Boosting Adversarial Attacks with MomentumLegacy edge — source detail unavailable
- Explainable AI: A Review of Machine Learning Interpretability Methods→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- Explainable AI: A Review of Machine Learning Interpretability Methods→Accessorize to a CrimeLegacy edge — source detail unavailable
- Explainable AI: A Review of Machine Learning Interpretability Methods→ZOOLegacy edge — source detail unavailable
- Explainable AI: A Review of Machine Learning Interpretability Methods→One Pixel Attack for Fooling Deep Neural NetworksLegacy edge — source detail unavailable
- Explainable AI: A Review of Machine Learning Interpretability Methods→Boosting Adversarial Attacks with MomentumLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| The Limitations of Deep Learning in Adversarial Settings | 2016 | 3,980 |
| Random sampling with a reservoir | 1985 | 1,781 |
| Accessorize to a Crime | 2016 | 1,596 |
What cites it, inside the corpus
Links
Topics
| Adversarial Robustness in Machine Learning | Computer Science |
| Advanced Malware Detection Techniques | Computer Science |
| Anomaly Detection Techniques and Applications | Computer Science |
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- supports5 author record(s) attached.
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Provenance
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