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Signal classification is a universal problem in adversarial wireless scenarios, especially when an eavesdropping radio receiver attempts to glean information about a target transmitter’s patterns, attributes, and contents over a wireless channel. In recent years, research surrounding the idea of Machine Learning (ML)-based signal classification has focused on modulation classification, with the downstream objective of demodulation. However, while the computer vision data domain has made significant progress in ensuring robust classification of images despite crafted perturbations, this success has not been translated to secure modulation classification. In this work, we perform the first-ever physical test of an eavesdropping ML-based modulation classifier radio, which we trained offline using a ensemble of i.i.d. models. Each model is trained with a weighted mixture of data perturbed by iterative, “least likely” white …