mlsanityData to 7 Oct 2026

Dropout Attacks

arXiv 2309.01614 ↗First posted 4 Sep 2023cs.LG cs.CRcited by 2

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Abstract (arXiv)

Dropout is a common operator in deep learning, aiming to prevent overfitting by randomly dropping neurons during training. This paper introduces a new family of poisoning attacks against neural networks named DROPOUTATTACK. DROPOUTATTACK attacks the dropout operator by manipulating the selection of neurons to drop instead of selecting them uniformly at random. We design, implement, and evaluate four DROPOUTATTACK variants that cover a broad range of scenarios. These attacks can slow or stop training, destroy prediction accuracy of target classes, and sabotage either precision or recall of a target class. In our experiments of training a VGG-16 model on CIFAR-100, our attack can reduce the precision of the victim class by 34.6% (from 81.7% to 47.1%) without incurring any degradation in model accuracy

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 64 references that are in the map. First posted on arXiv 4 Sep 2023.

Builds on

27 of 64 references.

References · 27

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
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  5. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  6. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  7. 2017Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoningcited by 1,012
  8. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  9. 2015Variational Dropout and the Local Reparameterization Trickcited by 489
  10. 2017Concrete Dropoutcited by 227
  11. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 219
  12. 2019Detecting AI Trojans Using Meta Neural Analysiscited by 192
15 more
  1. 2020Witches' Brew: Industrial Scale Data Poisoning via Gradient Matchingcited by 156
  2. 2020Backdoor Attacks Against Deep Learning Systems in the Physical Worldcited by 129
  3. 2020Adversarial Machine Learning -- Industry Perspectivescited by 121
  4. 2016Asynchronous Stochastic Gradient Descent with Delay Compensationcited by 98
  5. 2022Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoningcited by 93
  6. 2020An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networkscited by 92
  7. 2018Is feature selection secure against training data poisoning?cited by 88
  8. 2018Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attackscited by 78
  9. 2021Manipulating SGD with Data Ordering Attackscited by 73
  10. 2020Subpopulation Data Poisoning Attackscited by 68
  11. 2019Learning Sparse Networks Using Targeted Dropoutcited by 40
  12. 2022Indiscriminate Data Poisoning Attacks on Neural Networkscited by 24
  13. 2020Humpty Dumpty: Controlling Word Meanings via Corpus Poisoningcited by 17
  14. 2020Towards Class-Oriented Poisoning Attacks Against Neural Networkscited by 8
  15. 2017The Efficient Server Audit Problem, Deduplicated Re-execution, and the Webcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Rounding-Guided Backdoor Injection in Deep Learning Model Quantizationcited by 1
  2. 2026One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learningcited by 0