We evaluate the robustness of a Neural Architecture Search (NAS) algorithm known as Efficient NAS (ENAS) against data agnostic poisoning attacks on the original search space with carefully designed ineffective operations. We empirically demonstrate how our one shot search space poisoning approach exploits design flaws in the ENAS controller to degrade predictive performance on classification tasks. With just two poisoning operations injected into the search space, we inflate prediction error rates for child networks upto 90% on the CIFAR-10 dataset.

翻译：我们评估神经结构搜索算法(NAS)是否可靠,该算法被称为“高效NAS”(ENAS),它与原始搜索空间的不可知中毒袭击数据相对应,而原始搜索空间则经过精心设计的无效操作。我们从经验上表明,我们的一次搜索空间中毒方法是如何利用ENAS控制器的设计缺陷来降低分类任务的预测性能的。只要将两次中毒行动注入搜索空间,我们就会在CIFAR-10数据集中将儿童网络的预测误差率提高到90%。