Abstract: DNS manipulation is one of the most prevalent and effective techniques for censoring Internet access and interfering with users' online activities worldwide. Reliable detection of DNS tampering is crucial, but challenging due to evolving censorship tactics and the lack of complete ground truth data. In this paper, we demonstrate the power of machine learning (ML) in addressing these challenges by applying supervised and unsupervised models to recent global DNS measurement data collected by the Open Observatory of Network Interference (OONI).