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The following chart illustrates the Fake-O-Meter score across the audio over different time slices, each a length of 3.23 seconds. This allows for a more fine-grained analysis of the audio file. The X-axis (left to right) represents the time, and the Y-axis (bottom to top) represents the Fake-O-Meter score. The higher the value, the more likely it is that the audio at this specific time is AI-generated.

Analysis of the audio file over time

Interpreting the results

The DeepfakeTotal model assigned an average deepfake score of . This indicates that the soundtrack in the video has a likelihood of being a deepfake with probability percent (out of 100%). Scores nearing zero suggest authentic, non-fake human speech, while scores approaching 100% point towards the audio being a fake. However, there may be different speakers, as well as non-speech activity, so it is useful to take a look at the score distribution over time, as indicated in the plot above.

What has been analysed?

The analysis was solely conducted on the audio waveform between second 0 and second 120. No information beyond this duration, nor any metadata like filename, recording date, etc., was considered. Additionally, the video component has not been analysed (yet).

On the 'DeepfakeTotal' model

A machine learning model trained to identify audio deepfakes.

Limitations

Please note: the outcomes provided here are still in the experimental stage. This is partly because current deepfake detection models have limited generalization capabilities, as they do not (yet) effectively adapt to the rapidly evolving nature of new deepfakes that emerge daily. For more information, see our paper on the subject. Additionally, the model focuses solely on analyzing the audio track and does not evaluate the video component. Consequently, if the voice in the audio is that of a voice actor mimicking someone else's speech, it is not identified as a synthetically generated voice by our system.