  The rapid progress of deep speech synthesis models has posed significant
threats to society such as malicious content manipulation. Therefore, many
studies have emerged to detect the so-called deepfake audio. However, existing
works focus on the binary detection of real audio and fake audio. In real-world
scenarios such as model copyright protection and digital evidence forensics, it
is needed to know what tool or model generated the deepfake audio to explain
the decision. This motivates us to ask: Can we recognize the system
fingerprints of deepfake audio? In this paper, we present the first deepfake
audio dataset for system fingerprint recognition (SFR) and conduct an initial
investigation. We collected the dataset from the speech synthesis systems of
seven Chinese vendors that use the latest state-of-the-art deep learning
technologies, including both clean and compressed sets. In addition, to
facilitate the further development of system fingerprint recognition methods,
we provide extensive benchmarks that can be compared and research findings. The
dataset will be publicly available. .
