Adversarial robustness of dental panoramic radiograph classifiers against gradient-based perturbations

Wenya Long, Teng He and Yingjuan Xie

SAP Artificial Intelligence in Dentistry, 2027

Abstract: Introduction: Deep learning classifiers for dental radiographs are increasingly deployed, but their robustness to adversarial perturbations is unmeasured. Methods: A frozen ResNet-18 backbone and a linear head were trained on 182 annotated teeth from the DENTEX dataset and attacked with the fast gradient sign method and projected gradient descent at budgets from 1/255 to 16/255, comparing a baseline model, an adversarially trained model and a median-filter defense. Results: The baseline classified 63.6% of held-out teeth correctly. Both attacks flipped every correctly classified tooth at a budget of 1/255. The fast gradient sign method fell to 77.1% attack success at 16/255, whereas projected gradient descent remained at 100%. Neither adversarial training nor the median filter reduced attack success, and adversarial training lowered clean accuracy to 54.5%. Conclusions: A transfer-learning dental radiograph classifier with a frozen backbone and linear head is catastrophically vulnerable to adversarial perturbations, and the simple defenses tested do not restore robustness.

Date: 2027
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:aidart:aid2027186

DOI: 10.62486/aid2027186

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