mlsanityData to 13 Oct 2026

Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation

arXiv 2307.07269 ↗First posted 14 Jul 2023eess.IV cs.CV cs.LGcited by 6

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Abstract (arXiv)

It is imperative to ensure the robustness of deep learning models in critical applications such as, healthcare. While recent advances in deep learning have improved the performance of volumetric medical image segmentation models, these models cannot be deployed for real-world applications immediately due to their vulnerability to adversarial attacks. We present a 3D frequency domain adversarial attack for volumetric medical image segmentation models and demonstrate its advantages over conventional input or voxel domain attacks. Using our proposed attack, we introduce a novel frequency domain adversarial training approach for optimizing a robust model against voxel and frequency domain attacks. Moreover, we propose frequency consistency loss to regulate our frequency domain adversarial training that achieves a better tradeoff between model's performance on clean and adversarial samples. Code is publicly available at https://github.com/asif-hanif/vafa.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 14 Jul 2023.

Builds on

24 of 30 references.

References · 24

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,082
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,336
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,379
  5. 2013Intriguing properties of neural networkscited by 5,137
  6. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,992
  7. 2016Adversarial examples in the physical worldcited by 1,767
  8. 2020Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attackscited by 1,445
  9. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 957
  10. 2016Adversarial Machine Learning at Scalecited by 935
  11. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 615
  12. 2017Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methodscited by 545
12 more
  1. 2017Countering Adversarial Images using Input Transformationscited by 519
  2. 2018Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Surveycited by 460
  3. 2019Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networkscited by 238
  4. 2020Medical Image Segmentation Using Deep Learning: A Surveycited by 164
  5. 2020Torchattacks: A PyTorch Repository for Adversarial Attackscited by 157
  6. 2019Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systemscited by 152
  7. 2022UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentationcited by 112
  8. 2018Deflecting Adversarial Attacks with Pixel Deflectioncited by 71
  9. 2021AdvDrop: Adversarial Attack to DNNs by Dropping Informationcited by 55
  10. 2021Convolution-Free Medical Image Segmentation using Transformerscited by 49
  11. 2021Towards Robust General Medical Image Segmentationcited by 14
  12. 2020Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examplescited by 5

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2025On the Robustness of Medical Vision-Language Models: Are they Truly Generalizable?cited by 10
  2. 2024Adversarial Training: A Surveycited by 6
  3. 2025Beyond Single-Point Judgment: Distribution Alignment for LLM-as-a-Judgecited by 4
  4. 2026Lost in Volume: The CT-SpatialVQA Benchmark for Evaluating Semantic-Spatial Understanding of 3D Medical Vision-Language Modelscited by 4
  5. 2024On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Modelscited by 2
  6. 2026Lights, Camera, Malfunction: When Illumination Robustness Leaves VLA Models Blind to Colorcited by 1