第五代（5G）网络必须支持数十亿个异质设备，同时保证最佳服务质量（QoS）。这样的要求是不可能单独满足人类努力的，而机器学习（ML）代表了5G中的核心资产。然而，已知ML容易受到对抗例子的影响。此外，正如我们的论文所表明的那样，5G上下文暴露于另一种类型的对抗ML攻击，而现有威胁模型无法正式化。由于缺乏可用于对抗性ML研究的ML供电的5G设备，因此对此类风险的积极评估也有挑战性。为了解决这些问题，我们提出了一种新型的对抗ML威胁模型，该模型特别适合5G场景，不可知ML所解决的精确函数。与现有的ML威胁模型相反，我们的攻击不需要对目标5G系统的任何妥协，同时由于QoS保证和5G网络的开放性质仍然可行。此外，我们为基于公共数据的现实ML安全评估提供了一个原始框架。我们主动评估我们的威胁模型对5G中设想的ML的6个应用。我们的攻击会影响训练和推理阶段，可能会降低最先进的ML系统的性能，并且与以前的攻击相比，进入障碍较低。
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Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.
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