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This paper investigates the adversarial robustness of ResNet-based architectures (BrainNet, BrainNeXt, and DilationNet) for brain tumor classification against FGSM and PGD attacks. The study evaluates model performance across different MRI data preprocessing configurations, including full-sized augmented, shrunk augmented, and shrunk non-augmented datasets. The key finding is that BrainNeXt models demonstrate the highest robustness to black-box attacks, while BrainNet and DilationNet are more vulnerable, and that shrunk and non-augmented data significantly reduce model resilience.
Shrinking MRI data to improve brain tumor classification speed can drastically reduce adversarial robustness, even when standard accuracy remains high.
Adversarial robustness in deep learning models for brain tumor classification remains an underexplored yet critical challenge, particularly for clinical deployment scenarios involving MRI data. In this work, we investigate the susceptibility and resilience of several ResNet-based architectures, referred to as BrainNet, BrainNeXt and DilationNet, against gradient-based adversarial attacks, namely FGSM and PGD. These models, based on ResNet, ResNeXt, and dilated ResNet variants respectively, are evaluated across three preprocessing configurations (i) full-sized augmented, (ii) shrunk augmented and (iii) shrunk non-augmented MRI datasets. Our experiments reveal that BrainNeXt models exhibit the highest robustness to black-box attacks, likely due to their increased cardinality, though they produce weaker transferable adversarial samples. In contrast, BrainNet and Dilation models are more vulnerable to attacks from each other, especially under PGD with higher iteration steps and $\alpha$ values. Notably, shrunk and non-augmented data significantly reduce model resilience, even when the untampered test accuracy remains high, highlighting a key trade-off between input resolution and adversarial vulnerability. These results underscore the importance of jointly evaluating classification performance and adversarial robustness for reliable real-world deployment in brain MRI analysis.