可靠的皮肤癌诊断模型在早期筛查和医疗干预中起着重要作用。现有的计算机辅助皮肤癌分类系统采用了深度学习方法。然而，最近的研究发现它们对敌对攻击极其脆弱，敌对攻击往往会对皮肤癌诊断模型的性能产生无法察觉的扰动。为了缓解这些威胁，本文提出了一个简单、有效和资源高效的防御框架，通过对皮肤癌图像中的敌对扰动进行逆向工程。具体而言，首先建立了一个多尺度图像金字塔，以更好地保留医学成像领域的差异化结构。为了中和敌对影响，然后通过向不同尺度的皮肤图像注入各向同性高斯噪声来逐渐扩散敌对样本，将其移动到干净图像空间中。关键是，为了进一步逆向敌对噪声并抑制冗余注入的噪声，还精心设计了一种新颖的多尺度去噪机制，它从相邻尺度聚合图像信息。我们在ISIC 2019上评估了我们方法的防御效果，这是一个最大的皮肤癌多类别分类数据集。实验结果表明，所提出的方法能够成功逆转不同攻击的敌对扰动，并在防御皮肤癌诊断模型方面显著优于一些最先进的方法。 版权所有 ©2023 Elsevier Ltd. 保留所有权利。

Reliable skin cancer diagnosis models play an essential role in early screening and medical intervention. Prevailing computer-aided skin cancer classification systems employ deep learning approaches. However, recent studies reveal their extreme vulnerability to adversarial attacks - often imperceptible perturbations to significantly reduce the performances of skin cancer diagnosis models. To mitigate these threats, this work presents a simple, effective, and resource-efficient defense framework by reverse engineering adversarial perturbations in skin cancer images. Specifically, a multiscale image pyramid is first established to better preserve discriminative structures in the medical imaging domain. To neutralize adversarial effects, skin images at different scales are then progressively diffused by injecting isotropic Gaussian noises to move the adversarial examples to the clean image manifold. Crucially, to further reverse adversarial noises and suppress redundant injected noises, a novel multiscale denoising mechanism is carefully designed that aggregates image information from neighboring scales. We evaluated the defensive effectiveness of our method on ISIC 2019, a largest skin cancer multiclass classification dataset. Experimental results demonstrate that the proposed method can successfully reverse adversarial perturbations from different attacks and significantly outperform some state-of-the-art methods in defending skin cancer diagnosis models.Copyright © 2023 Elsevier Ltd. All rights reserved.