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Efficient wide-area surveillance of the maritime environment requires automated detection and classification techniques. Deep learning can be exploited for this purpose, but it is susceptible to adversarial attacks that can cause misclassifications. Diffusion models have previously been shown to improve robustness against adversarial perturbations in electro-optic imagery. This work investigates how such adversarial defence techniques perform against white-box attacks on a high-resolution Synthetic Aperture Radar (SAR) dataset. Using a maritime SAR dataset, we demonstrate the benefits of applying a diffusion model, including improved classification performance and reduced sea clutter.