Who Cited It

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

2016 · arXiv (Cornell University) · 1,416 citations · 7 from inside this corpus

Nicolas Papernot, Patrick McDaniel, Ian Goodfellow

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversaria… (2016)Transferability in Machine Le…Distilling the Knowledge in a Neural Network (2015)Distilling the Knowledge in a…Machine learning a probabilistic perspective (2012)Machine learning a probabilis…The Limitations of Deep Learning in Adversarial Settings (2016)The Limitations of Deep Learn…Model compression (2006)Model compressionRandom sampling with a reservoir (1985)Random sampling with a reserv…Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2020)Federated Learning in Mobile …Robust Physical-World Attacks on Deep Learning Visual Classification (2018)Robust Physical-World Attacks…Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (2018)Threat of Adversarial Attacks…Adversarial Examples: Attacks and Defenses for Deep Learning (2019)Adversarial Examples: Attacks…ZOO (2017)ZOOTowards Deep Learning Models Resistant to Adversarial Attacks (2017)Towards Deep Learning Models …Adversarial Examples Are Not Easily Detected (2017)Adversarial Examples Are Not …
12 of 12 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
this paper works it cites works citing it node size = global citations · hover for the full title

What this paper cites, inside the corpus

PaperYearCited
Distilling the Knowledge in a Neural Network201514,099
Machine learning a probabilistic perspective20129,324
The Limitations of Deep Learning in Adversarial Settings20163,980
Model compression20062,118
Random sampling with a reservoir19851,781

What cites it, inside the corpus

PaperYearCited
Federated Learning in Mobile Edge Networks: A Comprehensive Survey20202,538
Robust Physical-World Attacks on Deep Learning Visual Classification20182,154
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey20182,090
Adversarial Examples: Attacks and Defenses for Deep Learning20191,786
ZOO20171,779
Towards Deep Learning Models Resistant to Adversarial Attacks20171,536
Adversarial Examples Are Not Easily Detected20171,428

Links

DOI · Open access copy · OpenAlex record

Topics

Adversarial Robustness in Machine LearningComputer Science
Anomaly Detection Techniques and ApplicationsComputer Science
Advanced Malware Detection TechniquesComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports3 author record(s) attached.
  • supports16 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:59+00:00.

sha256 59007f057c7dae86…