mlsanityData to 14 Oct 2026

Machine Beats Machine: Machine Learning Models to Defend Against Adversarial Attacks

arXiv 2209.13963 ↗First posted 28 Sep 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

We propose using a two-layered deployment of machine learning models to prevent adversarial attacks. The first layer determines whether the data was tampered, while the second layer solves a domain-specific problem. We explore three sets of features and three dataset variations to train machine learning models. Our results show clustering algorithms achieved promising results. In particular, we consider the best results were obtained by applying the DBSCAN algorithm to the structured structural similarity index measure computed between the images and a white reference image.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 28 Sep 2022.

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References · 7

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 678
  3. 2019Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systemscited by 153
  4. 2020Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factorscited by 40
  5. 2015Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Controlcited by 33
  6. 2022Towards Adversarially Robust Deep Image Denoisingcited by 13
  7. 2018Subset Scanning Over Neural Network Activationscited by 2

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