Synthetic-augmented adversarial training for robust chest x-ray classification

Abstract CNN-based classifiers have become an essential tool in medical imaging for tasks such as chest X-ray classification. However, their widespread adoption is hindered by two significant challenges: vulnerability to adversarial attacks and poor generalization to unseen data. To address these issues, this study proposes a novel framework called Synthetic-Augmented Adversarial Training (SADA), which integrates FGSM-based adversarial training with synthetic image augmentation using a fine-tuned Stable Diffusion model. This approach aims to enhance both model robustness against perturbations and generalization capabilities by increasing training data diversity. Two CNN architectures, ConvNeXt-B and ConvNeXt-S, are trained under three different dataset configurations: clean images only; clean with adversarial images; and our proposed clean with adversarial and synthetic images. The models are evaluated under both white-box and cross-model FGSM attacks using an unseen chest X-ray dataset. The results demonstrate that under a cross-model attack on unseen data with an epsilon ( $$\epsilon$$ ) value of 0.2, models trained with the SADA framework achieved an F1-score of 0.8437 , representing a 17.40% improvement over the Clean + Adversarial configuration. These findings support the hypothesis that synthetic data helps address the generalization gaps of traditional adversarial training. Our findings suggest that combining adversarial and synthetic data offers a promising and scalable strategy for developing more reliable CNN-based diagnostic systems.