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Reliable skin cancer diagnosis models play an essential role in earlyscreening and medical intervention. Prevailing computer-aided skin cancerclassification systems employ deep learning approaches. However, recent studiesreveal their extreme vulnerability to adversarial attacks -- oftenimperceptible perturbations to significantly reduce the performances of skincancer diagnosis models. To mitigate these threats, this work presents asimple, effective, and resource-efficient defense framework by reverseengineering adversarial perturbations in skin cancer images. Specifically, amultiscale image pyramid is first established to better preserve discriminativestructures in the medical imaging domain. To neutralize adversarial effects,skin images at different scales are then progressively diffused by injectingisotropic Gaussian noises to move the adversarial examples to the clean imagemanifold. Crucially, to further reverse adversarial noises and suppressredundant injected noises, a novel multiscale denoising mechanism is carefullydesigned that aggregates image information from neighboring scales. Weevaluated the defensive effectiveness of our method on ISIC 2019, a largestskin cancer multiclass classification dataset. Experimental results demonstratethat the proposed method can successfully reverse adversarial perturbationsfrom different attacks and significantly outperform some state-of-the-artmethods in defending skin cancer diagnosis models.

Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism