  With the advancement of audio generation, generative models can produce
highly realistic audios. However, the proliferation of deepfake general audio
can pose negative consequences. Therefore, we propose a new task, deepfake
general audio detection, which aims to identify whether audio content is
manipulated and to locate deepfake regions. Leveraging an automated
manipulation pipeline, a dataset named FakeSound for deepfake general audio
detection is proposed, and samples can be viewed on website
https://FakeSoundData.github.io. The average binary accuracy of humans on all
test sets is consistently below 0.6, which indicates the difficulty humans face
in discerning deepfake audio and affirms the efficacy of the FakeSound dataset.
A deepfake detection model utilizing a general audio pre-trained model is
proposed as a benchmark system. Experimental results demonstrate that the
performance of the proposed model surpasses the state-of-the-art in deepfake
speech detection and human testers.
