Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow
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Citation edge evidence
- Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
- Robust Physical-World Attacks on Deep Learning Visual Classification→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
- Adversarial Examples: Attacks and Defenses for Deep Learning→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
- ZOO→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
- Towards Deep Learning Models Resistant to Adversarial Attacks→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
- Adversarial Examples Are Not Easily Detected→Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…Legacy edge — source detail unavailable
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…→Machine learning a probabilistic perspectiveLegacy edge — source detail unavailable
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…→Model compressionLegacy edge — source detail unavailable
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria…→Random sampling with a reservoirLegacy edge — source detail unavailable
- Distilling the Knowledge in a Neural Network→Model compressionLegacy edge — source detail unavailable
- The Limitations of Deep Learning in Adversarial Settings→Machine learning a probabilistic perspectiveLegacy edge — source detail unavailable
- Towards Deep Learning Models Resistant to Adversarial Attacks→Adversarial Examples Are Not Easily DetectedLegacy edge — source detail unavailable
- ZOO→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
- ZOO→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- ZOO→Towards Deep Learning Models Resistant to Adversarial AttacksLegacy 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
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→Distilling the Knowledge in a Neural NetworkLegacy 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→Towards Deep Learning Models Resistant to Adversarial AttacksLegacy edge — source detail unavailable
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey→ZOOLegacy edge — source detail unavailable
- Adversarial Examples: Attacks and Defenses for Deep Learning→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
- Adversarial Examples: Attacks and Defenses for Deep Learning→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
- Adversarial Examples: Attacks and Defenses for Deep Learning→ZOOLegacy edge — source detail unavailable
- Adversarial Examples: Attacks and Defenses for Deep Learning→Adversarial Examples Are Not Easily DetectedLegacy edge — source detail unavailable
- Adversarial Examples Are Not Easily Detected→The Limitations of Deep Learning in Adversarial SettingsLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Distilling the Knowledge in a Neural Network | 2015 | 14,099 |
| Machine learning a probabilistic perspective | 2012 | 9,324 |
| The Limitations of Deep Learning in Adversarial Settings | 2016 | 3,980 |
| Model compression | 2006 | 2,118 |
| Random sampling with a reservoir | 1985 | 1,781 |
What cites it, inside the corpus
Links
Download BibTeX · Download RIS · DOI · Open access copy · OpenAlex record
Topics
| Adversarial Robustness in Machine Learning | Computer Science |
| Anomaly Detection Techniques and Applications | Computer Science |
| Advanced Malware Detection Techniques | Computer Science |
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Provenance
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