mlsanityData to 7 Oct 2026

Adversarial Robustness on Image Classification with k-means

arXiv 2312.09533 ↗First posted 15 Dec 2023cs.LG cs.CR cs.CVcited by 0

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Abstract (arXiv)

In this paper we explore the challenges and strategies for enhancing the robustness of k-means clustering algorithms against adversarial manipulations. We evaluate the vulnerability of clustering algorithms to adversarial attacks, emphasising the associated security risks. Our study investigates the impact of incremental attack strength on training, introduces the concept of transferability between supervised and unsupervised models, and highlights the sensitivity of unsupervised models to sample distributions. We additionally introduce and evaluate an adversarial training method that improves testing performance in adversarial scenarios, and we highlight the importance of various parameters in the proposed training method, such as continuous learning, centroid initialisation, and adversarial step-count.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 15 Dec 2023.

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References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  6. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  7. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  8. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  9. 2016Universal adversarial perturbationscited by 774
  10. 2017Evasion Attacks against Machine Learning at Test Timecited by 686
  11. 2017One pixel attack for fooling deep neural networkscited by 579
  12. 2016Delving into Transferable Adversarial Examples and Black-box Attackscited by 530
12 more
  1. 2017Countering Adversarial Images using Input Transformationscited by 517
  2. 2016Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samplescited by 439
  3. 2017Mitigating Adversarial Effects Through Randomizationcited by 382
  4. 2018Certified Defenses against Adversarial Examplescited by 305
  5. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 234
  6. 2018Adversarial Robustness Toolbox v1.0.0cited by 214
  7. 2017The Space of Transferable Adversarial Examplescited by 156
  8. 2019Image Super-Resolution as a Defense Against Adversarial Attackscited by 64
  9. 2022"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practicecited by 55
  10. 2018Is Data Clustering in Adversarial Settings Secure?cited by 16
  11. 2019Suspicion-Free Adversarial Attacks on Clustering Algorithmscited by 11
  12. 2022On the Role of Generalization in Transferability of Adversarial Examplescited by 9

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