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The rapid progress of deep speech synthesis models has posed significantthreats to society such as malicious content manipulation. Therefore, manystudies have emerged to detect the so-called deepfake audio. However, existingworks focus on the binary detection of real audio and fake audio. In real-worldscenarios such as model copyright protection and digital evidence forensics, itis needed to know what tool or model generated the deepfake audio to explainthe decision. This motivates us to ask: Can we recognize the systemfingerprints of deepfake audio? In this paper, we present the first deepfakeaudio dataset for system fingerprint recognition (SFR) and conduct an initialinvestigation. We collected the dataset from the speech synthesis systems ofseven Chinese vendors that use the latest state-of-the-art deep learningtechnologies, including both clean and compressed sets. In addition, tofacilitate the further development of system fingerprint recognition methods,we provide extensive benchmarks that can be compared and research findings. Thedataset will be publicly available. .

System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation