mlsanityData to 7 Oct 2026

A Survey on Poisoning Attacks Against Supervised Machine Learning

arXiv 2202.02510 ↗First posted 5 Feb 2022cs.CR cs.LGcited by 5

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Abstract (arXiv)

With the rise of artificial intelligence and machine learning in modern computing, one of the major concerns regarding such techniques is to provide privacy and security against adversaries. We present this survey paper to cover the most representative papers in poisoning attacks against supervised machine learning models. We first provide a taxonomy to categorize existing studies and then present detailed summaries for selected papers. We summarize and compare the methodology and limitations of existing literature. We conclude this paper with potential improvements and future directions to further exploit and prevent poisoning attacks on supervised models. We propose several unanswered research questions to encourage and inspire researchers for future work.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 5 Feb 2022.

Builds on

14 of 21 references.

References · 14

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  3. 2016Understanding deep learning requires rethinking generalizationcited by 1,657
  4. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 500
  5. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 219
  6. 2019A backdoor attack against LSTM-based text classification systemscited by 214
  7. 2018Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learningcited by 203
  8. 2020Sponge Examples: Energy-Latency Attacks on Neural Networkscited by 108
  9. 2018Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attackscited by 78
  10. 2021Manipulating SGD with Data Ordering Attackscited by 73
  11. 2018Label Sanitization against Label Flipping Poisoning Attackscited by 68
  12. 2017Generative Poisoning Attack Method Against Neural Networkscited by 49
2 more
  1. 2016Adversary Resistant Deep Neural Networks with an Application to Malware Detectioncited by 9
  2. 2018A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filterscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 0; 2026: 1.

5 papers.

  1. 2024Deep Learning Model Security: Threats and Defensescited by 4
  2. 2024The last Dance : Robust backdoor attack via diffusion models and bayesian approachcited by 3
  3. 2024Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approachcited by 3
  4. 2024Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Camerascited by 3
  5. 2026Let the Neurons Die: Exploiting ReLU-Induced Model Degradationcited by 0