通过多尺度扩散与去噪聚合机制逆转皮肤癌对抗样本
Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism
AI总结：
针对皮肤癌诊断模型易受对抗攻击的问题，提出一种多尺度扩散与去噪聚合的防御框架，通过逆向工程对抗扰动，在ISIC 2019数据集上有效逆转多种攻击并优于现有方法。
摘要：
可靠的皮肤癌诊断模型在早期筛查和医疗干预中发挥着至关重要的作用。目前流行的计算机辅助皮肤癌分类系统采用深度学习方法。然而，近期研究揭示了它们对对抗性攻击的极端脆弱性——通常难以察觉的扰动就能显著降低皮肤癌诊断模型的性能。为缓解这些威胁，本工作提出了一种简单、有效且资源高效的防御框架，通过逆向工程皮肤癌图像中的对抗性扰动来实现防御。具体而言，首先建立多尺度图像金字塔，以更好地保留医学成像领域中具有判别性的结构。为了中和对抗效应，不同尺度的皮肤图像随后通过注入各向同性高斯噪声逐步进行扩散，将对抗样本移至干净图像流形上。关键的是，为了进一步逆转对抗噪声并抑制冗余的注入噪声，我们精心设计了一种新颖的多尺度去噪机制，该机制聚合来自相邻尺度的图像信息。我们在ISIC 2019（最大的皮肤癌多类分类数据集）上评估了所提方法的防御有效性。实验结果表明，所提出的方法能够成功逆转来自不同攻击的对抗性扰动，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。
英文摘要：
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.
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