mlsanityData to 7 Oct 2026

Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE

arXiv 2310.07084 ↗First posted 10 Oct 2023cs.LGcited by 4

On the map: Diffusion models → Robustness, privacy and adversarial aspects

Abstract (arXiv)

Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the training data distribution. In this work, we investigate the robustness of density estimation using the probability flow (PF) neural ordinary differential equation (ODE) model against gradient-based likelihood maximization attacks and the relation to sample complexity, where the compressed size of a sample is used as a measure of its complexity. We introduce and evaluate six gradient-based log-likelihood maximization attacks, including a novel reverse integration attack. Our experimental evaluations on CIFAR-10 show that density estimation using the PF ODE is robust against high-complexity, high-likelihood attacks, and that in some cases adversarial samples are semantically meaningful, as expected from a robust estimator.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Robustness, privacy and adversarial aspects, one of 14 directions in Diffusion models. Builds on 42 of its 43 references that are in the map. First posted on arXiv 10 Oct 2023.

Builds on

42 of 43 references.

Closest ancestors · 5

Same direction, newest first

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Other references · 37

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
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  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  6. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  7. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  8. 2022Flow Matching for Generative Modelingcited by 5,426
  9. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  10. 2018Neural Ordinary Differential Equationscited by 3,733
  11. 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
  12. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
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  4. 2021Maximum Likelihood Training of Score-Based Diffusion Modelscited by 670
  5. 2020On Adaptive Attacks to Adversarial Example Defensescited by 547
  6. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 542
  7. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 514
  8. 2022Improving Diffusion Models for Inverse Problems using Manifold Constraintscited by 501
  9. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 451
  10. 2019On Evaluating Adversarial Robustnesscited by 442
  11. 2019Your Classifier is Secretly an Energy Based Model and You Should Treat it Like Onecited by 402
  12. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
  13. 2019Input complexity and out-of-distribution detection with likelihood-based generative modelscited by 186
  14. 2018Towards the first adversarially robust neural network model on MNISTcited by 104
  15. 2022CARD: Classification and Regression Diffusion Modelscited by 102
  16. 2020Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimationcited by 87
  17. 2020Adversarial Robustness on In- and Out-Distribution Improves Explainabilitycited by 73
  18. 2023Robust Evaluation of Diffusion-Based Adversarial Purificationcited by 65
  19. 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Modelscited by 46
  20. 2020Further Analysis of Outlier Detection with Deep Generative Modelscited by 31
  21. 2021Robust Feature-Level Adversaries are Interpretability Toolscited by 27
  22. 2020Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradientscited by 22
  23. 2022On the Limitations of Stochastic Pre-processing Defensescited by 21
  24. 2020Adversarial Counterfactual Learning and Evaluation for Recommender Systemcited by 16
  25. 2022Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Datacited by 3

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4 papers.

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