Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation
Journal:
arXiv
Published Date:
Feb 6, 2025
Abstract
Deep learning-based medical image segmentation models, such as U-Net, rely on
high-quality annotated datasets to achieve accurate predictions. However, the
increasing use of generative models for synthetic data augmentation introduces
potential risks, particularly in the absence of rigorous quality control. In
this paper, we investigate the impact of synthetic MRI data on the robustness
and segmentation accuracy of U-Net models for brain tumor segmentation.
Specifically, we generate synthetic T1-contrast-enhanced (T1-Ce) MRI scans
using a GAN-based model with a shared encoding-decoding framework and
shortest-path regularization. To quantify the effect of synthetic data
contamination, we train U-Net models on progressively "poisoned" datasets,
where synthetic data proportions range from 16.67% to 83.33%. Experimental
results on a real MRI validation set reveal a significant performance
degradation as synthetic data increases, with Dice coefficients dropping from
0.8937 (33.33% synthetic) to 0.7474 (83.33% synthetic). Accuracy and
sensitivity exhibit similar downward trends, demonstrating the detrimental
effect of synthetic data on segmentation robustness. These findings underscore
the importance of quality control in synthetic data integration and highlight
the risks of unregulated synthetic augmentation in medical image analysis. Our
study provides critical insights for the development of more reliable and
trustworthy AI-driven medical imaging systems.
