一句话：提出面向KNN数据投毒鲁棒性的系统化测试方法，结合抽象域过近似分析与具体域测试，可证伪并认证鲁棒性。
数据投毒通过污染训练集改变机器学习组件对测试输入的预测，现有鲁棒性判定方法要么精度不足、要么运行缓慢，并且只能认证部分真正鲁棒的案例，认证失败时仍无法判断是否非鲁棒。本文针对K近邻（KNN）提出一种系统化测试方法：先在抽象域进行过近似分析以快速缩小搜索空间，再在具体域执行系统化测试以发现真实违例，因此既能证伪也能认证数据投毒鲁棒性。作者在多个监督学习数据集上评估该方法，结果表明其显著优于当前最优技术，并能对大多数测试输入判定KNN预测结果的数据投毒鲁棒性。
Data poisoning aims to compromise a machine learning based software component by contaminating its training set to change its prediction results for test inputs. Existing methods for deciding data-poisoning robustness have either poor accuracy or long running time and, more importantly, they can only certify some of the truly-robust cases, but remain inconclusive when certification fails. In other words, they cannot falsify the truly-non-robust cases. To overcome this limitation, we propose a systematic testing based method, which can falsify as well as certify data-poisoning robustness for a widely used supervised-learning technique named k-nearest neighbors (KNN). Our method is faster and more accurate than the baseline enumeration method, due to a novel over-approximate analysis in the abstract domain, to quickly narrow down the search space, and systematic testing in the concrete domain, to find the actual violations. We have evaluated our method on a set of supervised-learning datasets. Our results show that the method significantly outperforms state-of-the-art techniques, and can decide data-poisoning robustness of KNN prediction results for most of the test inputs.