Who Cited It

Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks

2019 · 1,404 citations · 1 from inside this corpus

Bolun Wang, Yuanshun Yao low, Shawn Shan, Huiying Li, Bimal Viswanath, Hai-Tao Zheng, Ben Y. Zhao

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.

Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks (2019)Neural Cleanse: Identifying a…Adam: A Method for Stochastic Optimization (2014)Adam: A Method for Stochastic…[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics… (2015)[No title in the source recor…On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Deg… (2024)On a Method to Measure Superv…Intriguing properties of neural networks (2013)Intriguing properties of neur…On Assessing ML Model Robustness: A Methodological Framework (Academic Track) (2025)On Assessing ML Model Robustn…The Limitations of Deep Learning in Adversarial Settings (2016)The Limitations of Deep Learn…Universal Adversarial Perturbations (2017)Universal Adversarial Perturb…Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks (2018)Feature Squeezing: Detecting …Advances and Open Problems in Federated Learning (2020)Advances and Open Problems in…
9 of 9 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.
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What this paper cites, inside the corpus

PaperYearCited
Adam: A Method for Stochastic Optimization201484,698
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…201550,396
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Deg…202413,528
Intriguing properties of neural networks20135,739
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)20254,755
The Limitations of Deep Learning in Adversarial Settings20163,980
Universal Adversarial Perturbations20172,744
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks20181,882

What cites it, inside the corpus

PaperYearCited
Advances and Open Problems in Federated Learning20205,383

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.

  • supports7 author record(s) attached.
  • supports81 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

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

sha256 59007f057c7dae86…