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Training deep neural networks (DNNs) requires large datasets and powerful computing resources, which has led some owners to restrict redistribution without permission. Watermarking techniques that embed confidential data into DNNs have been used to protect ownership, but these can degrade model performance and are vulnerable to watermark removal attacks. Recently, DeepJudge was introduced as an alternative approach to measuring the similarity between a suspect and a victim model. While DeepJudge shows promise in addressing the shortcomings of watermarking, it primarily addresses situations where the suspect model copies the victim’s architecture. In this study, we introduce DeepTaster, a novel DNN fingerprinting technique, to address scenarios where a victim’s data is unlawfully used to build a suspect model. DeepTaster can effectively identify such DNN model theft attacks, even when the …